Apparatus, system and method for active acoustic control (AAC) based on neural network (NN)

Abstract

For example, a controller of an active acoustic control (AAC) system may be configured to process input information including AAC configuration information and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations. For example, the controller may be configured to process the input information based on the plurality of noise inputs to determine a sound control mode for controlling sound within a sound control region. For example, the controller may include a neural network (NN) trained to generate an NN output based on an NN input, where the NN input is based on the AAC configuration information. For example, the controller may be configured to generate the sound control pattern based on the NN output, and output the sound control pattern to one or more acoustic transducers.

Description

[0001] Cross-references

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 356,395, filed on June 28, 2022, entitled “APPARATUS, SYSTEM, AND METHOD OF NEURAL-NETWORK (NN) BASED ACTIVE ACOUSTIC CONTROL (AAC),” the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] Aspects described herein generally relate to neural network (NN) based active acoustic control (AAC). Background Art

[0004] Active Noise Control (ANC) is a technology that uses digitally generated noise to reduce unwanted noise. It is based on the principle of sound wave superposition. Generally speaking, sound is a wave, which propagates in space. If another second sound wave with the same amplitude as the first sound wave but opposite phase can be generated, the first sound wave can be completely cancelled. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] For simplicity and clarity of illustration, the elements shown in the drawings are not necessarily drawn to scale. For example, the dimensions of some elements may be exaggerated relative to other elements for clarity of representation. In addition, reference numerals may be repeated in the drawings to indicate corresponding or similar elements. The drawings are listed below:

[0006] Figure 1 is a schematic block diagram illustration of an active acoustic control (AAC) system, according to some illustrative aspects.

[0007] Figure 2 Based on some illustrative aspects Figure 1 Schematic illustration of a deployment scheme of the components of an AAC system.

[0008] Figure 3 is a schematic illustration of a training scheme for training a neural network (NN) based AAC system, according to some illustrative aspects.

[0009] Figure 4 is a schematic block diagram illustration of a NN-based AAC system, according to some illustrative aspects.

[0010] Figure 5 is a schematic block diagram illustration of a NN-based prediction filter (PF), according to some illustrative aspects.

[0011] Figure 6is a schematic block diagram illustration of a PF NN according to some illustrative aspects.

[0012] Figure 7 is a schematic block diagram illustration of a NN-based AAC system, according to some illustrative aspects.

[0013] Figure 8 is a schematic block diagram illustration of a NN-based PF according to some illustrative aspects.

[0014] Fig. 9 is a schematic illustration of an AAC parameter extraction scheme for extracting AAC parameter information according to some illustrative aspects.

[0015] Fig.10 is a schematic block diagram illustration of a NN-based AAC system, according to some illustrative aspects.

[0016] Fig.11 is a schematic block diagram illustration of a training scheme for training a transducer transfer function NN, according to some illustrative aspects.

[0017] Fig.12 is a schematic block diagram illustration of an AAC system according to some demonstrative aspects.

[0018] Fig.13 is a schematic illustration depicting a graph of active noise reduction (ANR) versus time for a NN-based AAC system, according to some illustrative aspects.

[0019] Fig.14A is a schematic block diagram illustration of a NN-based controller according to some illustrative aspects.

[0020] Fig. 14B is a schematic block diagram illustration of a training scheme for training an acoustic feedback (AFB) mitigator NN, according to some illustrative aspects.

[0021] Fig.15 is a schematic block diagram illustration of a NN-based multiple-input multiple-output (MIMO) prediction unit, according to some demonstrative aspects.

[0022] Fig.16 is a schematic illustration of an implementation of components of a controller for an AAC system, according to some demonstrative aspects.

[0023] Fig.17 is a schematic block diagram illustration of a controller according to some illustrative aspects.

[0024] Fig.18 is a schematic illustration of a vehicle including an AAC system, according to some illustrative aspects.

[0025] Fig.19is a schematic flowchart illustration of a method of NN-based AAC according to some illustrative aspects.

[0026] Fig. 20 is a schematic block diagram illustration of the manufacture of a product according to some illustrative aspects. DETAILED DESCRIPTION

[0027] In the following detailed description, numerous specific details are set forth to provide a thorough understanding of some aspects. However, one of ordinary skill in the art will appreciate that some aspects may be practiced without these specific details. In other cases, well-known methods, procedures, components, units, and / or circuits are not described in detail to avoid making the discussion unclear.

[0028] Discussions herein utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “creating,” “analyzing,” “examining,” and the like may refer to the operation and / or processing of a computer, computing platform, computing system, or other electronic computing device that manipulates and / or transforms data represented as physical (e.g., electronic) quantities within the computer's registers and / or memories into other data that are similarly represented as physical quantities within the computer's registers and / or memories or other information storage media that may store instructions for performing the operations and / or processes.

[0029] As used herein, the terms “plurality” and “a plurality” include, for example, “a plurality” or “two or more.” For example, “a plurality of items” includes two or more items.

[0030] The words “exemplary” and “illustrative” are used herein to mean “serving as an example, instance, illustration, or illustration.” Any aspect, aspect, or design described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other aspects, aspects, or designs.

[0031] References to "one aspect," "aspects," "illustrative aspects," "various aspects," etc. indicate that the described aspects may include a particular feature, structure, or characteristic, but not every aspect necessarily includes the particular feature, structure, or characteristic. Furthermore, repeated use of the phrase "in one aspect" does not necessarily refer to the same aspect, but may.

[0032] As used herein, unless otherwise specified, the use of ordinal adjectives "first," "second," "third," etc. to describe common objects merely indicates that different instances of the same object are referenced and is not intended to imply that the objects so described must have a given sequence in time, space, in rank, or in any other manner.

[0033] As used herein, the term "data" may be understood to include information in any suitable analog or digital form, for example, provided as a file, a portion of a file, a collection of files, a signal or stream, a portion of a signal or stream, a collection of signals or streams, etc. In addition, the term "data" may also be used to refer to a reference to information, such as in the form of a pointer. However, the term data is not limited to the above examples, and may take various forms and / or represent any information as understood in the art.

[0034] The term "processor" or "controller" may be understood to include any type of technical entity that allows processing of any suitable type of data and / or information. Data and / or information may be processed according to one or more specific functions performed by a processor or controller. In addition, a processor or controller may be understood to be any type of circuit, such as any type of analog or digital circuit. Therefore, a processor or controller may be or include an analog circuit, a digital circuit, a mixed signal circuit, a logic circuit, a processor, a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), an integrated circuit, an application specific integrated circuit (ASIC), etc., or any combination thereof. Any other type of implementation of the corresponding functions described in more detail below may also be understood as a processor, a controller or a logic circuit. It should be understood that any two (or more) of the processors, controllers or logic circuits described in detail herein may be implemented as a single entity having equivalent functions, etc., and conversely, any single processor, controller or logic circuit described in detail herein may be implemented as two (or more) separate entities having equivalent functions, etc.

[0035] The term "memory" is understood to be a computer-readable medium (e.g., a non-transitory computer-readable medium) in which data or information may be stored for retrieval. Thus, references to "memory" may be understood to refer to volatile or non-volatile memory, including random access memory (RAM), read-only memory (ROM), flash memory, solid-state memory, magnetic tape, hard drive, optical drive, etc., or any combination thereof. Registers, shift registers, processor registers, data buffers, etc. are also included in the term memory. The term "software" may be used to refer to any type of executable instructions and / or logic, including firmware.

[0036] Some portions of the detailed description that follows are presented in terms of algorithms and symbolic representations of operations on data bits or binary digital signals within a computer memory. These algorithmic descriptions and representations may be techniques used by those skilled in the data processing arts to convey the substance of their work to others skilled in the art.

[0037] Here, an algorithm is generally considered to be a self-consistent sequence of actions or operations leading to a desired result. These involve physical manipulations of physical quantities. Typically, but not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. At times, it has proven convenient, primarily for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc. It will be understood, however, that all of these and similar terms will be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities.

[0038] As used herein, the term "circuit" may refer to, be a part of, or include an application specific integrated circuit (ASIC), an integrated circuit, an electronic circuit, a processor (shared, dedicated, or group) and / or memory (shared, dedicated, or group) that executes one or more software or firmware programs, a combinational logic circuit, and / or other suitable hardware components that provide the described functionality. In some aspects, some functions associated with the circuit may be implemented by one or more software or firmware modules. In some aspects, the circuit may include logic that is at least partially operable in hardware.

[0039] The term "logic" may refer to, for example, computing logic embedded in circuits of a computing device and / or computing logic stored in a memory of a computing device. For example, the logic may be accessed by a processor of a computing device to execute the computing logic to perform computing functions and / or operations. In one example, the logic may be embedded in various types of memory and / or firmware, such as silicon blocks of various chips and / or processors. The logic may be included in various circuits and / or implemented as part of various circuits, such as processor circuits, control circuits, etc. In one example, the logic may be embedded in volatile memory and / or non-volatile memory, including random access memory, read-only memory, programmable memory, magnetic memory, flash memory, and / or persistent memory, etc. The logic may be executed by one or more processors using memory (e.g., registers, buffers, stacks, etc.) coupled to the one or more processors, for example, executing the logic as needed.

[0040] Some illustrative aspects include devices, systems, and methods that may be effectively implemented for controlling noise (e.g., reducing or eliminating unwanted noise), such as noise in one or more frequency ranges (e.g., generally low, mid, and / or high frequencies), as described below.

[0041] Some illustrative aspects may include devices, methods, and / or systems configured for active acoustic control (AAC) to control acoustic energy and / or amplitude at one or more locations, for example, as described below.

[0042] In some illustrative aspects, an AAC system may be configured to control the acoustic energy and / or amplitude of one or more acoustic modes produced by one or more sound sources, which may include known and / or unknown sound sources, e.g., as described below.

[0043] In some illustrative aspects, the AAC system can be configured as an active noise control (ANC) system and / or an active sound control (ASC) system, and / or can perform one or more functions of an ANC system and / or an ASC system, which can be configured to control, reshape, reduce and / or eliminate the noise energy and / or amplitude of one or more acoustic modes ("primary modes") generated by one or more noise sources, which one or more noise sources may include known and / or unknown noise sources, for example, as described below.

[0044] In some illustrative aspects, an AAC system can be configured to generate an acoustic control mode (also referred to as a "sound control mode" or "auxiliary mode"), e.g., including a destructive noise mode and / or any other sound control mode, e.g., as described below.

[0045] In some illustrative aspects, the AAC system can be configured to generate an acoustic control mode, for example, based on one or more of the primary modes, such that a controlled sound zone (e.g., a noise reduction zone, such as a quiet zone) can be created by a combination of an auxiliary mode and a primary mode, for example, as described below.

[0046] In some illustrative aspects, the AAC system is configured to control, reduce, reshape, and / or eliminate predefined locations, areas, or zones (also referred to as "sound control zones," "acoustic control zones," "noise control zones," "quiet zones," and / or "quiet bubbles"). TM ”), for example, as described below.

[0047] In some illustrative aspects, the AAC system can be configured to control, reduce, reshape, and / or eliminate acoustic energy and / or noise within a sound control zone, for example, even without having (e.g., without considering) and / or using a priori information about dominant modes and / or one or more noise sources, e.g., as described below.

[0048] For example, the AAC system can be configured to control, reduce, reshape and / or eliminate acoustic energy and / or noise within an acoustic control zone (sound control zone), for example, independently of, regardless of and / or without the need to know in advance one or more properties of one or more of the noise sources and / or one or more of the primary modes, such as the number, type, location and / or other properties of one or more of the primary modes and / or one or more of the noise sources, for example, as described below.

[0049] Described herein are some illustrative aspects of AAC systems and / or methods configured to reshape, reduce, and / or eliminate noise energy and / or amplitude of one or more acoustic modes within a quiet zone, for example, as described below.

[0050] However, in other aspects, the AAC and / or sound control system and / or method can be configured to control any other sound energy and / or amplitude of one or more acoustic modes within an acoustic control zone (sound control zone) in any other manner, for example, to influence, change and / or modify the sound energy and / or amplitude of one or more acoustic modes within a predefined zone, for example, as described below.

[0051] In one example, an AAC system and / or method can be configured to selectively reshape, reduce and / or eliminate the acoustic energy and / or amplitude of one or more types of acoustic modes within an acoustic control zone (sound control zone), and / or selectively increase and / or amplify the acoustic energy and / or amplitude of one or more other types of acoustic modes within the acoustic control zone; and / or selectively maintain and / or preserve the acoustic energy and / or amplitude of one or more other types of acoustic modes within the acoustic control zone, for example, as described below.

[0052] In some illustrative aspects, the AAC system can be configured as a sound control system (e.g., a personal sound control system (also referred to as a "personal sound bubble (PSB)") TM System")) and / or can perform one or more functions of the sound control system, which can be configured to generate a sound control mode, which sound control mode can be based on at least one audio input, for example, so that at least one personal sound zone can be created based on the audio input, for example, as described below.

[0053] In some illustrative aspects, the AAC system may be configured to control at least one predefined location, area, or zone (e.g., at least one PSB), for example, based on the audio to be heard by the user. TM ) in the sound. In one example, PSB TM It can be configured to include the user's head and / or areas near the ears, for example, as described below.

[0054] In some illustrative aspects, the AAC system can be configured to control the PSB TM A sound contrast between one or more first sound patterns and one or more second sound patterns, for example, as described below.

[0055] In some illustrative aspects, for example, an AAC system may be configured to control acoustic contrast between one or more first sound patterns and one or more second sound patterns of audio to be heard by a user, eg, as described below.

[0056] In some illustrative aspects, for example, the AAC system can be configured to, for example, TM Selectively increase and / or amplify the audio heard in PSB TM The acoustic energy and / or amplitude of one or more types of acoustic modes within the acoustic signal to be reduced and / or eliminated; for example, selectively reshape, reduce and / or eliminate PSB based on the acoustic signal to be reduced and / or eliminated TM and / or selectively maintaining and / or preserving the PSB TM The sound energy and / or amplitude of one or more other types of acoustic modes within, for example, as described below.

[0057] In some illustrative aspects, the AAC system can be configured to control the PSB based on any other additional or alternative input or criteria. TM The sound within.

[0058] In some illustrative aspects, the AAC system may be configured to control, reshape, reduce, and / or eliminate the acoustic energy and / or amplitude of one or more of the primary modes within the sound control zone.

[0059] In some illustrative aspects, the AAC system can be configured to control, reshape, reduce and / or eliminate noise within a sound control area in a selective and / or configurable manner, for example based on one or more predefined noise pattern attributes, such that, for example, the noise energy, amplitude, phase, frequency, direction and / or statistical characteristics of one or more first primary modes may be affected by the auxiliary pattern, while the auxiliary pattern may have a reduced or even no effect on the noise energy, amplitude, phase, frequency, direction and / or statistical characteristics of one or more second primary modes, for example, as described below.

[0060] In some illustrative aspects, the AAC system can be configured to control, reshape, reduce and / or eliminate the acoustic energy and / or amplitude of a primary mode at one or more predefined locations within an acoustic control zone (sound control zone), such as a predefined envelope or enclosure that surrounds and / or encloses the acoustic control zone (sound control zone).

[0061] In one example, an acoustic control zone (sound control zone) may include a two-dimensional region, for example, defining an area in which the acoustic energy and / or amplitude of one or more of the primary modes is to be controlled, reshaped, reduced and / or eliminated.

[0062] According to this example, the AAC system can be configured to control, reshape, reduce and / or eliminate the acoustic energy and / or amplitude of a primary mode along a perimeter surrounding an acoustic control zone (sound control zone) and / or at one or more predefined locations within the acoustic control zone (sound control zone).

[0063] In one example, an acoustic control zone (sound control zone) may include a three-dimensional zone, for example, defining a volume in which the acoustic energy and / or amplitude of one or more of the primary modes are to be controlled, reshaped, reduced, and / or eliminated. According to this example, the AAC system may be configured to control, reshape, reduce, and / or eliminate the acoustic energy and / or amplitude of the primary modes on the surface of the enclosed three-dimensional volume.

[0064] In one example, the acoustic control zone (sound control zone) may include a spherical volume, and the AAC system may be configured to control, reshape, reduce and / or eliminate the acoustic energy and / or amplitude of the primary mode on the surface of the spherical volume.

[0065] In another example, the acoustic control zone (sound control zone) may include a cubic volume, and the AAC system may be configured to control, reshape, reduce and / or eliminate the acoustic energy and / or amplitude of the primary mode on the surface of the cubic volume.

[0066] In other aspects, an acoustic control zone (sound control zone) may include any other suitable volume that may be defined, for example, based on one or more properties of a location where the acoustic control zone is to be maintained.

[0067] Reference now Figure 1 , which schematically illustrates an AAC system 100 , according to some illustrative aspects.

[0068] Also refer to Figure 2 , which schematically illustrates a deployment scheme 200 of components of an AAC system according to some illustrative aspects. For example, the deployment scheme 200 may include Figure 1 Deployment of one or more elements (e.g., some or all elements) of the AAC system 100.

[0069] In some illustrative aspects, AAC system 100 may include an AAC system, an active noise cancellation (ANC) system, an acoustic control system, a sound control system, a PSB TM System and / or Quiet Bubble TM The system, as it operates and / or performs its functions, for example, as described below.

[0070] In some illustrative aspects, AAC system 100 may include a controller 102 (also referred to as an "AAC controller") for controlling sound within at least one AAC zone (also referred to as a "sound control zone" or "acoustic control zone") 110, e.g., as described in detail below.

[0071] In some illustrative aspects, controller 102 may include circuitry and / or logic (e.g., one or more processors including circuitry and / or logic, and / or memory circuitry and / or logic), or may be partially or fully implemented by such circuitry and / or such logic. Additionally or alternatively, one or more functions of controller 102 may be implemented by logic that may be executed by a machine and / or one or more processors, e.g., as described below.

[0072] In one example, the controller 102 may include, for example, at least one memory 198 coupled to one or more processors, which may be configured, for example, to at least temporarily store at least some information processed by the one or more processors and / or circuits, and / or may be configured to store logic to be used by the processors and / or circuits.

[0073] In one example, at least a portion of the functionality of the controller 102 may be implemented by an integrated circuit (eg, a chip, such as a system on a chip (SoC)).

[0074] In other aspects, the controller 102 may be implemented by any other logic and / or circuitry, and / or according to any other architecture.

[0075] In some demonstrative aspects, AAC region 110 can include an enclosed space, eg, as described below.

[0076] In some illustrative aspects, the enclosed space may include a portion or the entirety of a cabin of a vehicle, such as a car, bus, and / or truck, for example, as described below.

[0077] In some illustrative aspects, AAC zone 110 may include a portion of the space within a cabin of a vehicle.

[0078] In one example, the AAC zone 110 may include a space defined with respect to a person in a vehicle.

[0079] In one example, the AAC zone 110 may include a space defined about a seat in a vehicle.

[0080] For example, the AAC zone 110 may be defined to include a space around a driver's seat of a vehicle, a space around a headrest of a driver's seat of a vehicle, and / or a space around a head and / or ears of a driver of a vehicle.

[0081] For example, another AAC zone 110 may be defined to include the space around another seat of the vehicle (e.g., a passenger seat or back seat), and / or the space around the headrest of another seat of the vehicle, and / or the space around the head and / or ears of a passenger of the vehicle.

[0082] In other aspects, the AAC zone 110 can include substantially the entire space of the cabin of the vehicle.

[0083] In some illustrative aspects, the enclosed space can include any other passenger compartment, such as, for example, the cabin of an airplane, the cabin of a train, the cabin of a medical system, an area of ​​a room, and the like.

[0084] In other aspects, the enclosed space may include, for example, any other enclosed portion or area of ​​a vehicle or non-vehicle space.

[0085] In some illustrative aspects, sound control zone 110 can be located within a vehicle, and AAC system 100 can be deployed as part of the vehicle. In other aspects, sound control zone 110 can be located in any non-vehicle area or location.

[0086] In some illustrative aspects, sound control zone 110 can include a three-dimensional (3D) zone. For example, sound control zone 110 can include a spherical zone.

[0087] In another example, the sound control zone 110 may include any other 3D zone.

[0088] In some illustrative aspects, AAC system 100 may be configured to control sound and / or noise within zone 110 , for example, to provide an improved driving experience for a driver and / or one or more passengers of the vehicle, such as by controlling sound and / or noise within zone 110 in a manner that provides an improved music and / or sound experience within the vehicle, improved telephone conversation quality, etc.

[0089] In some demonstrative aspects, AAC controller 102 may include, or may be implemented with, input 191 , which may be configured to receive input information 195 , eg, as described below.

[0090] In some illustrative aspects, AAC controller 102 may include controller 193 configured to determine a sound control mode for controlling sound within at least one sound control zone 110 in a vehicle, for example, based on input information 195 , for example, as described below.

[0091] In some illustrative aspects, controller 193 may include circuitry and / or logic (e.g., one or more processors including circuitry and / or logic, and / or memory circuitry and / or logic), or may be partially or fully implemented by such circuitry and / or such logic. Additionally or alternatively, one or more functions of controller 193 may be implemented by logic that may be executed by a machine and / or one or more processors, e.g., as described below.

[0092] In some illustrative aspects, the input information 195 may include, for example, multiple noise inputs 104 from one or more acoustic sensors (also referred to as "primary sensors," "noise sensors," or "reference sensors") 119 that represent acoustic noise at multiple predefined noise sensing locations 105, for example, as described below.

[0093] In some illustrative aspects, the AAC controller 102 may receive noise input 104 from one or more acoustic sensors 119, which may include: one or more physical sensors (e.g., microphones, accelerometers, tachometers, etc.) located at one or more of the positions 105, represented by "N", and / or one or more virtual sensors configured to estimate acoustic noise at one or more of the positions 105, for example, as described below.

[0094] In some illustrative aspects, noise input 104 may be based on monitoring information that may be sensed by one or more monitoring sensors (denoted as “M”) at one or more monitoring locations 103 , such as microphones, accelerometers, tachometers, and the like, for example, as described below.

[0095] In some illustrative aspects, noise input 104 may include noise input corresponding to a virtual sensor at virtual sensor location 105. For example, the noise input corresponding to the virtual sensor at virtual sensor location 105 may be based on monitoring information sensed by one or more sensors at one or more monitoring locations 103, e.g., as described below.

[0096] In some demonstrative aspects, one or more monitoring locations 103 may include one or more locations other than noise sensing locations 105 , eg, as described below.

[0097] In some illustrative aspects, such as Figure 2 As shown, the monitoring positions 103 may include one or more monitoring positions 103 outside the sound control zone 110 , and / or one or more monitoring positions 103 inside the sound control zone 110 .

[0098] In one example, in a vehicle implementation of the AAC system 100, the one or more monitoring locations 103 may include, for example, a monitoring location in a cabin of the vehicle, a monitoring location on a roof of the vehicle, a monitoring location on a chassis of the vehicle, a monitoring location on an exterior surface of the vehicle, a monitoring location on a wheel of the vehicle, and / or any other additional or alternative locations.

[0099] In some illustrative aspects, input information 195 may include, for example, a plurality of residual noise inputs 106 from one or more physical and / or virtual residual noise acoustic sensors (also referred to as “error sensors” or “auxiliary sensors”) 121, the plurality of residual noise inputs representing acoustic residual noise at a plurality of predefined residual noise sensing locations 107. For example, residual noise sensing locations 107 may be located within sound control zone 110, e.g., as described below.

[0100] In some demonstrative aspects, input information 195 may include plurality of residual noise inputs 106 , eg, as described below.

[0101] In other aspects, one or more (e.g., some or all) of the plurality of residual noise inputs 106 may be optional and / or excluded. For example, in some illustrative aspects, the AAC controller 102 may be configured to provide a technical solution for controlling sound within the at least one AAC zone 110, e.g., even without using one or more (e.g., some or all) of the plurality of residual noise inputs 106, e.g., as described below.

[0102] In some illustrative aspects, the AAC controller 102 may receive residual noise input 106 from one or more acoustic sensors 121 located at one or more locations 107, which may include one or more physical sensors, such as microphones, accelerometers, tachometers, etc., and / or from one or more virtual sensors configured to estimate residual noise at one or more locations 107, for example, as described below.

[0103] In some illustrative aspects, residual noise input 104 may include a residual noise input corresponding to a virtual sensor at virtual sensor location 107. For example, the residual noise input corresponding to the virtual sensor at virtual sensor location 107 may be based on monitoring information sensed by one or more sensors at one or more monitoring locations 103, e.g., as described below.

[0104] In some illustrative aspects, AAC system 100 can include at least one acoustic transducer 108, such as a speaker, a shaker, and / or any other actuator. For example, AAC controller 102 can control acoustic transducer 108 to generate an acoustic sound control pattern configured to control sound within sound control zone 110, e.g., as described in detail below.

[0105] In some demonstrative aspects, at least one acoustic transducer 108 may include, for example, an array of one or more acoustic transducers, such as at least one suitable speaker, to generate a sound control pattern based on sound control signal 109 .

[0106] In some illustrative aspects, at least one acoustic transducer 108 may be located at one or more locations that may be determined based on, among other things, one or more properties of the sound control zone 110 (e.g., a size and / or shape of the zone 110), one or more expected property inputs 104, one or more expected properties of one or more potential actual noise sources 202 (e.g., an expected location and / or directionality of the noise source 202 relative to the sound control zone 110), and a number of noise sources 202.

[0107] In one example, acoustic transducer 108 may include a speaker array including a predefined number (denoted as Mspk) of speakers or multi-channel sound sources. In some illustrative aspects, acoustic transducer 108 may include a speaker array implemented using a suitable "compact sound source" located at a suitable location (e.g., outside zone 110).

[0108] In another example, the speaker array may be implemented using a plurality of speakers distributed in a space around, for example, the sound control zone 110 .

[0109] In some illustrative aspects, one or more locations 105 may be distributed in any combination of locations on and / or outside the spherical volume, such as one or more locations around the spherical volume, eg, as described below.

[0110] In some illustrative aspects, one or more locations 105 can be distributed outside of sound control zone 110. For example, one or more locations 105 can be distributed on or near an envelope or housing surrounding sound control zone 110.

[0111] For example, if the sound control zone 110 is defined by a spherical volume, one or more locations 105 may be distributed on the surface of the spherical volume and / or outside of the spherical volume.

[0112] In some illustrative aspects, one or more of locations 105 may be distributed at one or more other additional or alternative locations relative to sound control zone 110. In one example, in a vehicle implementation of AAC system 100, one or more noise sensing locations 105 may include, for example, a noise sensing location 105 in a cabin of the vehicle, a noise sensing location 105 on a chassis of the vehicle, a noise sensing location 105 on an exterior surface of the vehicle, a noise sensing location 105 on a wheel of the vehicle, and / or any other additional or alternative locations.

[0113] In some illustrative aspects, locations 107 may be distributed within sound control zone 110. In one example, one or more of locations 107 may be proximate to the envelope of sound control zone 110. In one example, one or more of locations 107 may be proximate to a head and / or ears of a user of sound control zone 110.

[0114] For example, if zone 110 is defined by a spherical volume, locations 107 may be distributed over the spherical surface having a radius that is smaller than the radius of sound control zone 110 .

[0115] In some illustrative aspects, the AAC system 100 may include one or more first acoustic sensors (“primary sensors”) 119 to sense acoustic noise at one or more noise sensing locations of the plurality of noise sensing locations 105 and / or to sense monitoring acoustic information at one or more monitoring locations 103 .

[0116] In some demonstrative aspects, AAC system 100 may include one or more second acoustic sensors (“error sensors”) 121 to sense acoustic residual noise at one or more residual noise sensing locations of plurality of residual noise sensing locations 107 , eg, as described below.

[0117] In other aspects, some or even all of acoustic sensors 121 may be optional or excluded, for example, as described below.

[0118] In some illustrative aspects, one or more of the error sensors and / or one or more of the primary sensors may be implemented using one or more of the "virtual sensors" ("virtual microphones"). The virtual microphones corresponding to particular microphone locations may be implemented using any suitable algorithm and / or method capable of evaluating an acoustic pattern that would be sensed by an actual acoustic sensor located at the particular microphone location.

[0119] In some demonstrative aspects, AAC controller 102 may be configured to simulate and / or perform the functionality of a virtual microphone, for example, by estimating and / or evaluating acoustic noise patterns at particular locations of the virtual microphone.

[0120] In some illustrative aspects, an AAC system (e.g., AAC system 100) ( Figure 1 ) may include a first array 219 of one or more primary sensors (e.g., microphones, accelerometers, tachometers, etc.) configured to sense a primary mode at one or more of locations 105 and / or locations 103. For example, array 219 may include a plurality of acoustic sensors 119 ( Figure 1 ). For example, array 219 may include a microphone to output noise signal 104 ( Figure 1 ), the noise signal comprises, for example, a sequence of Nmic samples per second. For example, if the microphone operates at a sampling rate of approximately 48KHz, Nmic may be, for example, 48,000 samples per second. The noise signal 104 ( Figure 1 ) may include any other suitable signal having any other suitable sampling rate and / or any other suitable properties.

[0121] In some illustrative aspects, one or more sensors of array 219 may be implemented using one or more "virtual sensors." For example, array 219 may be implemented by a combination of at least one microphone and at least one virtual microphone. A virtual microphone corresponding to a particular microphone position of position 105 may be implemented by any suitable algorithm and / or method capable of evaluating an acoustic pattern, such as, for example, as described in controller 102 ( Figure 1 ) or System 100( Figure 1 ), the acoustic pattern will be sensed by the acoustic sensor located at a specific microphone position. For example, the controller 102 ( Figure 1 ) may be configured based on at least one microphone 119 ( Figure 1 ) to evaluate the acoustic pattern of the virtual microphone.

[0122] In some illustrative aspects, AAC controller 102 may be configured to simulate and / or perform the functionality of a virtual master sensor at master sensor location 105 , for example, based on monitoring information sensed by one or more monitoring sensors at one or more monitoring locations 103 .

[0123] In some illustrative aspects, AAC system 100 ( Figure 1 ) may include a second array 221 of one or more error sensors (e.g., microphones) configured to sense acoustic residual noise at one or more locations 107. For example, the array 221 may include a plurality of acoustic sensors 121 ( Figure 1). For example, the error sensor may include one or more sensors to sense acoustic residual noise patterns on a spherical surface within the spherical sound control zone 110. In other aspects, some or even all of the acoustic sensors 221 may be optional or excluded.

[0124] In some illustrative aspects, one or more sensors of array 221 may be implemented using one or more "virtual sensors." For example, array 221 may include a combination of at least one microphone and at least one virtual microphone. A virtual microphone corresponding to a particular microphone position of position 107 may be implemented by any suitable algorithm and / or method capable of evaluating an acoustic pattern, such as, for example, as controller 102 ( Figure 1 ) or System 100( Figure 1 ), the acoustic pattern will be sensed by the acoustic sensor located at a specific microphone position. For example, the controller 102 ( Figure 1 ) may be configured based on at least one microphone 121 ( Figure 1 ) to evaluate the acoustic pattern of the virtual microphone.

[0125] In some illustrative aspects, AAC controller 102 may be configured to simulate and / or perform the functionality of a virtual master sensor at error sensor location 107 , for example, based on monitoring information sensed by one or more monitoring sensors at one or more monitoring locations 103 .

[0126] In some illustrative aspects, the number, location and / or distribution of locations 103, 105 and / or 107, and / or the number, location and / or distribution of one or more acoustic sensors at one or more locations 103, 105 and 107 can be determined based on the size of the sound control zone 110 and / or the envelope of the sound control zone 110, the shape of the sound control zone 110 or the envelope of the sound control zone 110, one or more properties of the acoustic sensors located at one or more locations 103, 105 and / or 107, such as the sampling rate of the sensor, etc.

[0127] In one example, one or more acoustic sensors, such as microphones, accelerometers, tachometers, etc., may be deployed at locations 103 , 105 , and / or 107 according to the spatial sampling theorem, such as defined by Equation 1 below.

[0128] For example, the number of master sensors, the distances between master sensors, the number of error sensors, and / or the distances between error sensors may be determined according to the spatial sampling theorem, for example, defined by Equation 1 below.

[0129] In one example, the main sensor and / or the error sensor may be distributed at a distance denoted by d from each other, such as equidistant or non-equidistant distribution. For example, the distance d may be determined as follows:

[0130]

[0131] Where c is the speed of sound, f max Indicates the maximum frequency where sound control is desired.

[0132] In one example, where the maximum frequency of interest is fmax=100 (Hz), the distance d may be determined as d<=(343) / (2*100)=1.7 (m).

[0133] like Figure 2 As shown, the deployment scheme 200 is configured for a circular or spherical sound control zone 110. For example, one or more positions 105 are distributed (e.g., substantially evenly distributed) around the sound control zone 110 in a spherical or circular manner, and positions 107 are distributed (e.g., substantially evenly distributed) within the sound control zone 110 in a spherical or circular manner.

[0134] However, in other aspects, components of AAC system 100 may be deployed according to any other deployment scheme, including any suitable distribution of locations 103, 105, and / or 107, e.g., configured relative to any other suitable form and / or shape of sound control zone.

[0135] In some illustrative aspects, controller 102 may be configured to determine a sound control mode based on at least one acoustic parameter (e.g., energy, amplitude, phase, frequency, direction, and / or statistical characteristics) within sound control zone 110, for example, as described in detail below.

[0136] In some illustrative aspects, the AAC controller 102 may determine a sound control mode for selectively reducing one or more predefined first acoustic modes (e.g., noise patterns) within the sound control zone 110 without reducing one or more second acoustic modes (e.g., noise patterns) within the sound control zone 110, for example, as described below.

[0137] In some illustrative aspects, the sound control zone 110 can be located inside the vehicle, and the AAC controller 102 can determine a sound control mode for selectively reducing one or more first acoustic modes (e.g., including road noise modes, wind noise modes, and / or engine noise modes) without reducing one or more second acoustic modes (e.g., including audio modes of audio devices located within the vehicle, horn noise modes, siren noise modes, hazard acoustic modes of hazards, warning acoustic modes of warning signals, acoustic modes of information signals, etc.).

[0138] In other aspects, the sound control zone 110 can be within any other (e.g., vehicular or non-vehicular) location and / or area, and the AAC controller 102 can be configured to determine a sound control mode for selectively reducing any other one or more first acoustic modes without substantially reducing or affecting any other one or more second acoustic modes.

[0139] In some illustrative aspects, AAC controller 102 may be configured to determine a sound control mode, for example, even without information related to one or more actual noise source properties of one or more actual noise sources 202 generating acoustic noise at noise sensing location 105 .

[0140] For example, the noise source properties may include a number of noise sources 202 , locations of noise sources 202 , types of noise sources 202 , and / or one or more properties and / or characteristics of one or more noise patterns generated by one or more of noise sources 202 .

[0141] In some illustrative aspects, the AAC controller 102 can be configured to determine a sound control mode, for example, while taking into account one or more factors, such as one or more acoustic transfer functions between elements of the AAC system 100, such as an acoustic transfer function between at least one acoustic transducer 108 and one or more acoustic sensing locations, for example, as described below.

[0142] In some illustrative aspects, an acoustic transfer function may represent and / or describe an acoustic medium through which a sound wave propagates. For example, a transfer function between a source point and a destination point may include a direct path, such as defined by a straight line connecting the source point and the destination point (if any), and / or one or more multipaths, such as an indirect path including reflections from objects in the environment surrounding the source point and the destination point.

[0143] In some illustrative aspects, the one or more acoustic sensing locations may include physical sensing locations of acoustic sensors. For example, the one or more acoustic transfer functions may include an acoustic transfer function between acoustic transducer 108 and an acoustic sensor physically located at the acoustic sensing location, for example, as described below.

[0144] In some illustrative aspects, the one or more acoustic sensing locations may include virtual sensing locations of virtual acoustic sensors. For example, the one or more acoustic transfer functions may include an acoustic transfer function between acoustic transducer 108 and a virtual acoustic sensor at a virtual acoustic sensing location, for example, as described below.

[0145] In some illustrative aspects, the one or more acoustic sensing locations may include a residual noise sensing location 107 of a residual noise sensor 121 (e.g., a physical residual noise sensor 121 or a virtual residual noise sensor 121), e.g., as described below. For example, the one or more acoustic transfer functions may include an acoustic transfer function between an acoustic transducer 108 and a residual noise sensing location 107, e.g., as described below.

[0146] In some illustrative aspects, the one or more acoustic sensing locations may include a noise sensing location 105 of a noise sensor 119 (e.g., a physical noise sensor 119 or a virtual noise sensor 119), e.g., as described below. For example, the one or more acoustic transfer functions may include an acoustic transfer function between an acoustic transducer 108 and a noise sensing location 105, e.g., as described below.

[0147] In some illustrative aspects, the one or more acoustic sensing locations may include monitoring sensing location 103 of monitoring sensor 119, e.g., as described below. For example, the one or more acoustic transfer functions may include an acoustic transfer function between acoustic transducer 108 and a monitoring sensing location at monitoring location 103, e.g., as described below.

[0148] In some illustrative aspects, AAC controller 102 may be configured to determine a sound control mode, for example while taking into account statistical characteristics of an acoustic signal (eg, noise) to be processed by AAC system 100 , eg, as described below.

[0149] In some demonstrative aspects, the statistical characteristics of an acoustic signal (eg, noise) to be processed by AAC system 100 may be based on a spectral distribution of the acoustic signal (eg, how the energy of the acoustic signal is distributed over a relevant frequency range).

[0150] In other aspects, the AAC controller 102 may be configured to determine the sound control mode based on any other additional or alternative factors, criteria, attributes, and / or parameters.

[0151] In some illustrative aspects, the acoustic transfer function within sound control zone 110 and / or in the environment of sound control zone 110 may be susceptible to physical changes to the environment of sound control zone 110 .

[0152] In some illustrative aspects, the acoustic transfer function in a vehicle environment may be susceptible to physical changes to the vehicle environment, such as the position and / or angle of vehicle seats, the number of occupants in the vehicle, one or more opened / closed windows, and / or any other additional or alternative properties of the vehicle environment, e.g., as described below.

[0153] In some illustrative aspects, the spectral distribution of an acoustic signal (e.g., a noise signal) in a vehicle environment may be sensitive to one or more factors including, for example, road surface, vehicle tire type, vehicle speed, vehicle engine speed (RPM), wind-induced noise, operation of the vehicle air conditioning system, and / or one or more additional or alternative factors, e.g., as described below.

[0154] In some illustrative aspects, AAC controller 102 may be configured to determine and / or set a sound control mode (e.g., set operation of AAC system 100 to predefined conditions), e.g., based on a predefined transfer function and / or a spectral distribution of noise, e.g., as described below.

[0155] In some illustrative aspects, the AAC controller 102 can be configured to determine and / or set a sound control mode (e.g., set the operation of the AAC system 100 to current or real-time conditions), for example, based on a current or real-time estimated transfer function and / or spectral distribution of noise, for example, as described below.

[0156] In some illustrative aspects, the AAC controller 102 can be configured to determine and / or set a sound control mode (e.g., set operation of the AAC system 100 to new conditions), for example, based on one or more changes in the transfer function and / or the spectral distribution of the noise, for example, as described below.

[0157] In some illustrative aspects, AAC controller 102 may be configured to set parameters of AAC system 100 , for example, in real-time and / or continuously (eg, in a manner that may address one or more technical issues), for example, as described below.

[0158] In some illustrative aspects, the AAC controller 102 may include a state machine, and / or may be configured to perform one or more operations and / or functions of a state machine, which may be configured to receive input from one or more sources (e.g., an onboard computer) and / or from one or more detectors that may monitor one or more environmental conditions, for example, as described below.

[0159] In one example, in a vehicle implementation, input from one or more sources may include information such as vehicle seat position, number of passengers, vehicle speed, engine speed, etc., for example, as described below.

[0160] In another example, input from one or more sources may include, for example, information indicative of temperature and / or pressure in the environment of the sound control zone 110 (eg, a cabin of a vehicle).

[0161] In some demonstrative aspects, AAC controller 102 may be configured to determine an operating mode of AAC system 100 , eg, by programming AAC system 100 with an appropriate set of parameters, eg, as described below.

[0162] In some demonstrative aspects, input information 195 may include AAC information 129 (also referred to as “AAC support information,” “AAC auxiliary information,” or “AAC configuration information”), which may be received from one or more information sources 120 .

[0163] For example, in a vehicle implementation, the information sources 120 may include one or more information sources in the vehicle, eg, as described below.

[0164] In one example, the AAC information may include environmental information corresponding to the environment of the vehicle.

[0165] In another example, the AAC information may include cabin information corresponding to the cabin of the vehicle.

[0166] In another example, the AAC information may include vehicle information corresponding to physical parameters of the vehicle.

[0167] In other aspects, the AAC information may include any other additional and / or alternative information affecting one or more parameters of the sound control zone 110 , and / or information corresponding to the environment of the sound control zone 110 .

[0168] In some demonstrative aspects, controller 193 may be configured to receive and process AAC information 129 , eg, via input 191 , eg, as described below.

[0169] In some demonstrative aspects, controller 193 may be configured to determine sound control signal 109 , eg, based on AAC information 129 , eg, in addition to noise input 104 and / or residual noise input 106 , eg, as described below.

[0170] In some demonstrative aspects, AAC information 129 may include information corresponding to the configuration of AAC within sound control zone 110 and / or one or more other sound control zones, eg, as described below.

[0171] In some demonstrative aspects, AAC information 129 may include information affecting one or more parameters and / or properties of an AAC configuration corresponding to sound control zone 110 , eg, as described below.

[0172] In some demonstrative aspects, AAC configuration information 129 may include information representative of a spectral distribution of acoustic signals within sound control zone 110 and / or in an environment of sound control zone 110 , eg, as described below.

[0173] In some demonstrative aspects, AAC configuration information 129 may include information representative of one or more parameters that affect real-time configuration of AAC within sound control zone 110 , eg, as described below.

[0174] In some demonstrative aspects, AAC configuration information 129 may include information representative of one or more physical characteristics of sound control zone 110 , eg, as described below.

[0175] In some demonstrative aspects, AAC configuration information 129 may include information representative of one or more acoustic characteristics of sound control zone 110 , eg, as described below.

[0176] In some demonstrative aspects, AAC configuration information 129 may include information from one or more information sources 120 other than one or more acoustic sensors of system 100 (eg, noise sensor 119 , residual noise sensor 121 , and / or monitoring sensors).

[0177] In some demonstrative aspects, AAC configuration information 129 may include information from one or more information sources 120 independent of one or more acoustic sensors (eg, noise sensor 119 , residual noise sensor 121 , and / or monitoring sensors) of system 100 .

[0178] In some demonstrative aspects, AAC assistance information 129 may include information that may be utilized by AAC controller 193 , eg, to assist AAC controller 193 in configuring one or more AAC settings and / or AAC parameters, eg, as described below.

[0179] In some demonstrative aspects, AAC auxiliary information 129 may include real-time input information, which may be received from one or more information sources 120 in real-time, eg, during operation of AAC system 100 , eg, as described below.

[0180] In some demonstrative aspects, AAC configuration information 129 may include real-time information corresponding to the real-time acoustic configuration of sound control zone 110 and / or other sound control zones, eg, as described below.

[0181] In some demonstrative aspects, AAC information 129 may include such information, which may correspond to, may be representative of, and / or may affect one or more sound control parameters of a sound control setting of sound control zone 110 , eg, as described below.

[0182] In some demonstrative aspects, AAC information 129 may include acoustic configuration information corresponding to the acoustic configuration of sound control zone 110 , eg, as described below.

[0183] In some demonstrative aspects, AAC configuration information 129 may include information representative of one or more parameters that affect real-time configuration of AAC within sound control zone 110 , eg, as described below.

[0184] In some demonstrative aspects, AAC configuration information 129 may be different from number of noise inputs 104 , eg, as described below.

[0185] In some demonstrative aspects, AAC configuration information 129 may be received separately from multiple noise inputs 104 , eg, as described below.

[0186] In some demonstrative aspects, AAC configuration information 129 may be received from one or more information sources 120 , which may be separate and / or distinct from reference noise sensor 119 , eg, as described below.

[0187] In some demonstrative aspects, AAC assistance information 129 may include acoustic configuration information, eg, including information related to one or more parameters of an acoustic configuration of sound control zone 110 , eg, as described below.

[0188] In some demonstrative aspects, AAC information 129 may include acoustic configuration information, eg, information including one or more parameters that define an acoustic configuration of sound control zone 110 , eg, as described below.

[0189] In some demonstrative aspects, AAC information 129 may include acoustic configuration information, eg, information including one or more parameters that affect the acoustic configuration of sound control zone 110 , eg, as described below.

[0190] In some demonstrative aspects, AAC assistance information 129 may include acoustic configuration information, eg, information including one or more parameters representative of an acoustic configuration of sound control zone 110 , eg, as described below.

[0191] In some demonstrative aspects, AAC assistance information 129 may include information corresponding to an AAC configuration affecting voice control zone 110 implemented in a vehicle, eg, as described below.

[0192] In some demonstrative aspects, AAC assistance information 129 may include vehicle system configuration information corresponding to configuration of operating modes of one or more vehicle systems of a vehicle including voice control zone 110 , eg, as described below.

[0193] In some demonstrative aspects, AAC auxiliary information 129 includes vehicle type information corresponding to a type of vehicle that includes sound control zone 110 , eg, as described below.

[0194] In some demonstrative aspects, AAC assistance information 129 may include vehicle sensor information from one or more vehicle sensors of a vehicle including sound control zone 110 , eg, as described below.

[0195] In some demonstrative aspects, AAC auxiliary information 129 may include vehicle speed information corresponding to a speed of a vehicle including sound control zone 110 , eg, as described below.

[0196] In some demonstrative aspects, AAC assistance information 129 may include engine information corresponding to an engine of a vehicle that includes sound control zone 110 , eg, as described below.

[0197] In some demonstrative aspects, AAC assistance information 129 may include brake system information corresponding to a brake system of a vehicle including voice control zone 110 , eg, as described below.

[0198] In some demonstrative aspects, AAC assistance information 129 may include road detection information from a road detection system of a vehicle including sound control zone 110 , eg, as described below.

[0199] In some demonstrative aspects, AAC assistance information 129 may include steering information corresponding to a steering system of a vehicle including voice control zone 110 , eg, as described below.

[0200] In some demonstrative aspects, AAC assistance information 129 may include tire information corresponding to one or more tires of a vehicle that includes sound control zone 110 , eg, as described below.

[0201] In some illustrative aspects, AAC auxiliary information 129 may include seat position information corresponding to one or more seats of a vehicle that includes voice control zone 110 , eg, as described below.

[0202] In some demonstrative aspects, AAC auxiliary information 129 may include passenger information corresponding to one or more passengers of a vehicle that includes sound control zone 110 , eg, as described below.

[0203] In some demonstrative aspects, AAC assistance information 129 may include open state information corresponding to an open state of a vehicle including sound control zone 110 , eg, as described below.

[0204] In some demonstrative aspects, AAC auxiliary information 129 may include audio system information corresponding to an audio system of a vehicle that includes sound control zone 110 , eg, as described below.

[0205] In some demonstrative aspects, AAC assistance information 129 may include climate information corresponding to at least one of a climate within sound control zone 110 or a climate outside sound control zone 110 , eg, as described below.

[0206] In some demonstrative aspects, AAC auxiliary information 129 may include user position information corresponding to a position of at least one of a head or an ear of a user in sound control zone 110 , eg, as described below.

[0207] In some demonstrative aspects, AAC auxiliary information 129 may include user identity information corresponding to a user identity to control user preferences with respect to sound control zone 110 , eg, as described below.

[0208] In one example, the AAC auxiliary information 129 may include user identity information corresponding to the user identity of the sound control zone 110. For example, the AAC auxiliary information 129 may include user identity information corresponding to the identity of the vehicle driver, for example, to control the user preference of the sound control zone 110 implemented with respect to the vehicle driver's seat.

[0209] In another example, the AAC auxiliary information 129 may include user identity information corresponding to a user identity to control user preferences regarding a sound control zone 110 that may be used by another user. For example, the AAC auxiliary information 129 may include user identity information corresponding to a vehicle driver identity, for example, to control user preferences regarding a sound control zone 110 implemented for one or more passenger seats of the vehicle.

[0210] In some demonstrative aspects, AAC assistance information 129 may include acoustic configuration information, eg, including any other additional or alternative information, which may be related to the acoustic configuration of sound control zone 110 , eg, as described below.

[0211] In some demonstrative aspects, input 191 may be configured to receive AAC auxiliary information 129 via system bus information received via a system bus of a vehicle including sound control zone 110 , eg, as described below.

[0212] In some illustrative aspects, input 191 may be configured to receive AAC auxiliary information 129 via a controller area network (CAN) bus message received via a CAN bus of the vehicle.

[0213] In some illustrative aspects, input 191 may be configured to receive AAC auxiliary information 129 via A to B (A2B) bus information received via an A2B bus of the vehicle.

[0214] In some demonstrative aspects, input 191 may be configured to receive AAC auxiliary information 129 via a Multimedia Oriented System Transport (MOST) bus message received via a MOST bus of the vehicle.

[0215] In some demonstrative aspects, input 191 may be configured to receive AAC auxiliary information 129 via wireless communication information received via a wireless communication link.

[0216] In some illustrative aspects, input 191 may be configured to receive AAC auxiliary information 129 via Ethernet bus information received via an Ethernet bus of the vehicle.

[0217] In other aspects, input 191 may be configured to receive AAC information 129 via any other wired link or connection, a wireless link or connection, and / or any other communication mechanism, connection, link, bus, and / or interface.

[0218] In some demonstrative aspects, AAC information 129 may include sensor information from one or more sensors, eg, as described below.For example, information source 120 may include one or more sensors, eg, as described below.

[0219] In some demonstrative aspects, AAC auxiliary information 129 may include sensor information from one or more acoustic sensors, eg, as described below.For example, information source 120 may include one or more acoustic sensors, eg, as described below.

[0220] In some illustrative aspects, information sources 120 may include one or more acoustic sensors, which may be different from and / or independent of monitoring sensors at monitoring locations 103 , noise acoustic sensors 119 , and / or residual noise acoustic sensors 121 , eg, as described below.

[0221] In some illustrative aspects, information source 120 may include one or more acoustic sensors that may be part of a monitoring sensor at monitoring location 103, noise acoustic sensor 119, and / or residual noise acoustic sensor 121, and / or may utilize one or more functionalities thereof, e.g., as described below.

[0222] In some demonstrative aspects, AAC information 129 may be based in part or in whole on acoustic information from one or more of noise acoustic sensor 119 and / or residual noise acoustic sensor 121 , eg, as described below.

[0223] In some illustrative aspects, information source 120 may include one or more environmental sensors that may be configured to sense one or more parameters and / or properties of the environment of sound control zone 110 , eg, as described below.

[0224] In some illustrative aspects, for example, the environmental sensor may include an acoustic sensor, an image sensor, an optical sensor, a light sensor, a temperature sensor, an accelerometer, a pressure sensor, a humidity sensor, and / or any other type of sensor.

[0225] In some illustrative aspects, AAC information 129 may include sensor information from one or more optical and / or image sensors, e.g., as described below. For example, information source 120 may include one or more optical and / or image sensors, such as cameras, e.g., as described below.

[0226] In some demonstrative aspects, AAC information 129 may include any other sensor information from any other additional or alternative sensors.

[0227] In some demonstrative aspects, information sources 120 may include one or more state information sources, which may be configured to provide AAC information 129 corresponding to states of one or more elements and / or settings affecting an AAC configuration, eg, as described below.

[0228] In some demonstrative aspects, AAC information 129 may include vehicle system configuration information corresponding to configuration of operation of one or more vehicle systems of a vehicle including voice control zone 110 , eg, as described below.

[0229] In some illustrative aspects, AAC information 129 may include vehicle system configuration information from one or more vehicle systems of the vehicle, e.g., as described below. For example, information source 120 may include one or more vehicle systems of the vehicle and / or a system controller of the vehicle, e.g., as described below.

[0230] In some demonstrative aspects, AAC information 129 may include vehicle type information corresponding to the type of vehicle, eg, as described below.

[0231] For example, the AAC information 129 may include vehicle type information indicating a vehicle type (e.g., sport utility vehicle (SUV), hatchback, crossover, convertible, sedan, coupe, sports car, minivan, van, station wagon, light truck, etc.).

[0232] For example, the AAC information 129 may include vehicle type information indicating a vehicle size type (eg, small, medium, large, wide, medium-width, narrow, tall, medium-height, low, etc.).

[0233] For example, the AAC information 129 may include vehicle type information indicating a vehicle manufacturer of the vehicle.

[0234] In some demonstrative aspects, AAC information 129 may include vehicle sensor information, which may be received from one or more sensors of a vehicle system of a vehicle, eg, as described below.

[0235] In some demonstrative aspects, AAC information 129 may include vehicle speed information corresponding to a vehicle speed, eg, as described below.

[0236] In some illustrative aspects, AAC information 129 may include engine information corresponding to an engine of the vehicle, eg, as described below.

[0237] For example, the AAC information 129 may include revolutions per minute (RPM) information corresponding to the RPM of an engine of the vehicle, eg, as described below.

[0238] In some illustrative aspects, AAC information 129 may include brake system information corresponding to a vehicle braking system, eg, as described below.

[0239] For example, the AAC information 129 may include brake system information to indicate operating status of a primary brake system, an emergency brake system, and / or an anti-lock brake system (ABS), and / or any other brake system, eg, as described below.

[0240] In some demonstrative aspects, AAC information 129 may include road detection information corresponding to a vehicle road detection system, eg, as described below.

[0241] For example, the AAC information 129 may include road detection information to indicate a road type, such as a flat road, a bumpy road, a rough road, a highway, a paved road, a dirt road, or a gravel road, etc., for example, as described below.

[0242] In some demonstrative aspects, AAC information 129 may include steering information corresponding to a vehicle steering system, eg, as described below.

[0243] For example, the AAC information 129 may include steering wheel information indicating a steering wheel angle of the vehicle, eg, as described below.

[0244] In some demonstrative aspects, AAC information 129 may include tire information corresponding to a tire system of the vehicle, eg, as described below.

[0245] For example, the AAC information 129 may include tire pressure information indicating the pressure of one or more tires of the vehicle and / or tire type information indicating the type and / or size of one or more tires of the vehicle, eg, as described below.

[0246] In some illustrative aspects, AAC information 129 may include seat information corresponding to one or more seats in a vehicle, eg, as described below.

[0247] For example, the AAC information 129 may include seat position information corresponding to the positioning of a driver's seat and / or the positioning of one or more passenger seats in a vehicle, eg, as described below.

[0248] For example, the AAC information 129 may include seat occupancy information corresponding to the occupancy of one or more seats in the vehicle, e.g., as described below. For example, the seat occupancy information may include information indicating how many seats are occupied, how many seats are unoccupied, which specific seats are occupied, etc.

[0249] In some demonstrative aspects, AAC information 129 may include passenger information corresponding to one or more passengers in the vehicle, eg, as described below.

[0250] For example, the AAC information 129 may include passenger information to indicate the count, position, location, size, and / or measurements of one or more passengers in the vehicle, eg, as described below.

[0251] In some demonstrative aspects, AAC information 129 may include opening status information corresponding to one or more openings of the vehicle, eg, as described below.

[0252] In some illustrative aspects, AAC information 129 may include window / roof information corresponding to windows, doors, trunk, and / or roof of the vehicle, eg, as described below.

[0253] For example, the AAC information 129 may include: window information to indicate a fully open position, a partially open position, a window opening degree (e.g., a window opening percentage), or a closed position of one or more windows; door information to indicate an open door or a closed door; and / or roof information to indicate a roof type (e.g., a metal roof or a panoramic roof), a roof position (e.g., an open position, a partially open position), a roof opening degree (e.g., a roof opening percentage), or a closed position of a vehicle roof, for example, as described below.

[0254] In some demonstrative aspects, AAC information 129 may include audio system information corresponding to a vehicle audio system, eg, as described below.

[0255] For example, the AAC information 129 may include audio system information to indicate one or more audio parameters of audio system operation, such as audio volume, audio input, equalizer settings, or music levels, for example, as described below.

[0256] In some demonstrative aspects, AAC information 129 may include climate information corresponding to the climate inside the vehicle and / or the climate outside the vehicle, eg, as described below.

[0257] For example, the AAC information 129 may include temperature information corresponding to the temperature inside the vehicle and / or the temperature outside the vehicle, eg, as described below.

[0258] For example, the AAC information 129 may include humidity information corresponding to humidity inside the vehicle and / or humidity outside the vehicle, eg, as described below.

[0259] For example, the AAC information 129 may include precipitation information corresponding to rain, snow, and / or ice conditions outside the vehicle, eg, as described below.

[0260] In some demonstrative aspects, AAC information 129 may include any other additional or alternative information.

[0261] In some illustrative aspects, controller 193 may be configured to determine a sound control mode to control sound within sound control zone 110 , eg, based on AAC information 129 , plurality of noise inputs 104 , and plurality of residual noise inputs 106 , eg, as described below.

[0262] In some illustrative aspects, AAC controller 102 can include output 197 to output a sound control pattern to a plurality of acoustic transducers. For example, output 197 can be configured to output the sound control pattern in the form of sound control signal 109 to control acoustic transducer 108, e.g., as described below.

[0263] In one example, the sound control signal 109 may include a plurality of transducer input signals to be respectively provided to the plurality of acoustic transducers 108. For example, the transducer input signals of the acoustic transducers 108 may be configured to cause the acoustic transducers 108 to output acoustic signals according to a sound control pattern. For example, the plurality of transducer input signals may be configured so that a sound control pattern may be generated by a combination of acoustic signals output by the plurality of acoustic transducers 108.

[0264] In some illustrative aspects, the AAC controller 102 may be configured to determine AAC parameter settings based on the AAC configuration information 129 and determine a sound control mode of the sound control signal 109, e.g., by applying the AAC parameter settings to at least one of the plurality of noise inputs 104 and / or the plurality of residual noise inputs 106, e.g., as described below.

[0265] In some illustrative aspects, the AAC parameter settings may include settings for a prediction filter, settings for a path transfer function, settings for an extractor (also referred to as an "acoustic mode extractor") to extract multiple reference acoustic modes, e.g., including non-overlapping reference acoustic modes, and / or settings for any other parameters that may be used to determine, generate, update, configure, and / or set a sound control mode for controlling the acoustic transducer 108, e.g., as described below.

[0266] In some demonstrative aspects, AAC controller 102 may be configured to determine a predictive filter setting for at least one predictive filter based on, for example, AAC configuration information 129 , and determine a sound control mode based on, for example, the predictive filter setting, eg, as described below.

[0267] In some demonstrative aspects, a prediction filter setting may include, for example, a plurality of prediction filter coefficients for configuring a prediction filter, eg, as described below.

[0268] In some demonstrative aspects, the plurality of prediction filter coefficients may be represented by a prediction filter weight vector, eg, as described below.

[0269] In some illustrative aspects, the prediction filter settings may include, for example, a prediction filter weight vector applied by the prediction filter to determine a sound control mode of the sound control signal 109, for example, based on at least one of the plurality of noise inputs 104 and / or the plurality of residual noise inputs 106, e.g., as described below.

[0270] In other aspects, the AAC controller 102 may be configured to determine any other additional or alternative prediction filter settings based on, for example, the AAC configuration information 129 .

[0271] In some illustrative aspects, the AAC controller 102 can be configured to determine path transfer function settings for one or more path transfer functions based on, for example, the AAC configuration information 129, and to determine a sound control mode of the sound control signal 109, for example, based on applying the path transfer function settings to at least one of the multiple noise inputs 104 and / or the multiple residual noise inputs 106, e.g., as described below.

[0272] In some demonstrative aspects, the path transfer function setting may include setting of a path transfer function between acoustic transducer 108 and an acoustic sensing location, eg, as described below.

[0273] In some demonstrative aspects, the path transfer function setting may include setting of a path transfer function between acoustic transducer 108 and a physical acoustic sensing location of a physical acoustic sensor, eg, as described below.

[0274] In some demonstrative aspects, the path transfer function setting may include setting of a path transfer function between acoustic transducer 108 and a virtual acoustic sensing location of a virtual acoustic sensor, eg, as described below.

[0275] In some illustrative aspects, the path transfer function setting may include a path transfer function setting between acoustic transducer 108 and noise sensing location 105 , eg, as described below.

[0276] In some demonstrative aspects, the path transfer function setting may include a path transfer function setting between acoustic transducer 108 and residual noise sensing location 107 , eg, as described below.

[0277] In some illustrative aspects, the path transfer function setting may include a path transfer function setting between acoustic transducer 108 and monitoring location 103. For example, at least one of the one or more residual noise inputs 106 may be based on a monitoring input sensed at monitoring location 103, for example.

[0278] For example, the AAC controller 102 may be configured to determine settings for a path transfer function between the acoustic transducer 108 and the monitoring location 103 of the monitoring sensor used to determine the residual noise input 106 .

[0279] For example, the AAC controller 102 may be configured to determine a sound control mode to control the sound within the sound control zone 110 based on, for example, the settings of a path transfer function between the acoustic transducer 108 and the monitoring location 103 of the monitoring sensor.

[0280] In one example, the monitoring location 103 of the monitoring sensor used to determine the residual noise input 106 may be within the sound control zone 110 , eg, as described above.

[0281] In another example, the monitoring location 103 of the monitoring sensor used to determine the residual noise input 106 may be outside of the sound control zone 110 , eg, as described above.

[0282] In some illustrative aspects, AAC controller 102 may be configured to determine a path transfer function setting for a path transfer function between acoustic transducer 108 and noise sensing location 105 , eg, as described below.

[0283] In some illustrative aspects, AAC controller 102 may be configured to determine a path transfer function setting for a path transfer function between acoustic transducer 108 and residual noise sensing location 107 , eg, as described below.

[0284] In some demonstrative aspects, AAC controller 102 may be configured to determine a noise extraction function based on, for example, AAC configuration information 129 , eg, as described below.

[0285] In some illustrative aspects, the AAC controller 102 can be configured to determine one or more extracted acoustic patterns, for example by applying a noise extraction function to at least one of the multiple noise inputs 104 and / or the multiple residual noise inputs 106, and to determine a sound control pattern of the sound control signal 109, for example based on the one or more extracted acoustic patterns, for example, as described below.

[0286] In some demonstrative aspects, AAC controller 102 may be configured to determine a sound control profile based on AAC configuration information 129 , and determine a sound control mode based on the sound control profile, eg, as described below.

[0287] In some illustrative aspects, the sound control profile may include settings for one or more sound control parameters, and the AAC controller 102 may be configured to determine a sound control mode of the sound control signal 109 based on the settings of the one or more sound control parameters according to the sound control profile, for example, as described below.

[0288] In some illustrative aspects, memory 198 may be configured, for example by controller 193, to store a plurality of sound control profiles corresponding to a plurality of sound control configurations, for example, as described below.

[0289] In some demonstrative aspects, controller 193 may be configured to select and retrieve a selected sound control profile from a plurality of sound control profiles in memory 198 , eg, based on AAC configuration information 129 , eg, as described below.

[0290] In some demonstrative aspects, controller 193 may be configured to determine a sound control mode for sound control signal 109 , for example, based on a selected sound control profile, eg, as described below.

[0291] In some demonstrative aspects, the plurality of sound control profiles may include one or more user-based profiles corresponding to one or more users, e.g., as described below.

[0292] In some demonstrative aspects, a user-based profile corresponding to a user may include settings for one or more sound control parameters based on user preferences, for example, as described below.

[0293] For example, a user-based profile corresponding to a user may include, for example, noise reduction settings, such as a noise reduction "on" setting that sets noise reduction to an active state, a noise reduction "off" setting that sets noise reduction to an inactive state, a noise reduction "low" setting that sets noise reduction to a low level of noise reduction, a noise reduction "high" setting that sets noise reduction to a high level of noise reduction, and the like.

[0294] For example, a user-based profile corresponding to a user may include settings for: noise mitigation for each user (e.g., each configured, e.g., a predefined user seating position), noise mitigation to be optimized for one or more specific seats (e.g., front seats or rear seats), noise mitigation optimized for one or more specific frequency bands, etc.

[0295] For example, a user-based profile corresponding to a user may include, for example, audio settings for audio to be heard within the sound control area 110 , such as the sound level of the audio, equalizer settings for the audio, and the like.

[0296] In some illustrative aspects, a user-based profile may correspond to a user and may allow the user to control user preferences regarding sound control zone 110 , eg, as described below.

[0297] In one example, the user-based profile may correspond to a user of the sound control zone 110. For example, a user-based profile for a vehicle driver may include settings for one or more sound control parameters based on the driver's preferences regarding the sound control zone 110 implemented relative to the vehicle driver's seat.

[0298] In another example, a user-based profile may correspond to a first user to control user preferences regarding a sound control zone 110 that may be used by a second user. For example, a user-based profile for a vehicle driver may include settings for one or more sound control parameters based on the driver's preferences regarding sound control zones 110 implemented relative to one or more passenger seats of the vehicle.

[0299] In some illustrative aspects, AAC configuration information 129 may include, for example, user identity information corresponding to a user identity. For example, controller 193 may be configured to select and retrieve a selected sound control profile from a plurality of sound control profiles in memory 198, for example based on the user identity information in AAC configuration information 129.

[0300] In some demonstrative aspects, AAC controller 102 may be configured to selectively mute a sound control mode of sound control signal 109 , eg, based on AAC configuration information 129 , eg, as described below.

[0301] In some demonstrative aspects, AAC controller 102 may be configured to adjust a volume of a sound control mode of sound control signal 109 , eg, based on AAC configuration information 129 , eg, as described below.

[0302] In some demonstrative aspects, AAC controller 102 may be configured to determine a setting of at least one AAC parameter, eg, based on AAC information 129 , and determine a sound control mode of sound control signal 109 , eg, based on the AAC parameter setting, eg, as described below.

[0303] In some illustrative aspects, the AAC parameter settings may include settings for a prediction filter, settings for a path transfer function, settings for adaptive AAC parameters, settings for extracting multiple reference acoustic modes, such as an extractor of non-overlapping reference acoustic modes (also referred to as an "acoustic mode extractor"), and / or settings for any other parameters that can be used to determine, generate, update, configure, and / or adapt a sound control mode for controlling the acoustic transducer 108, for example, as described below.

[0304] In some demonstrative aspects, AAC controller 102 may be configured to determine a predictive filter setting for at least one predictive filter based on AAC information 129 and, for example, determine a sound control mode for sound control signal 109 based on the predictive filter setting, eg, as described below.

[0305] In some illustrative aspects, the prediction filter settings may include, for example, a plurality of prediction filter coefficients and / or a prediction filter weight vector to be applied by a prediction filter for determining a sound control mode based on multiple noise inputs 104 and / or multiple residual noise inputs 106, e.g., as described below.

[0306] In some illustrative aspects, the AAC controller 102 may be configured to determine path transfer function settings for one or more path transfer functions based on the AAC information 129 and to apply the path transfer function settings to determine a sound control mode of the sound control signal 109 based on multiple noise inputs 104 and / or multiple residual noise inputs 106, e.g., as described below.

[0307] In some illustrative aspects, AAC controller 102 may be configured to determine a path transfer function setting for a path transfer function between acoustic transducer 108 and noise sensing location 105 , eg, based on AAC information 129 , eg, as described below.

[0308] In some demonstrative aspects, AAC controller 102 may be configured to determine a path transfer function setting for a path transfer function between acoustic transducer 108 and residual noise sensing location 107 , eg, based on AAC information 129 , eg, as described below.

[0309] In some illustrative aspects, AAC controller 102 may be configured to determine a path transfer function setting for a path transfer function between acoustic transducer 108 and monitoring location 103 , for example, based on AAC information 129 , eg, as described below.

[0310] In some demonstrative aspects, AAC controller 102 may be configured to extract a plurality of reference acoustic patterns from plurality of noise inputs 104 , eg, including a plurality of disjoint reference acoustic patterns that are statistically independent.

[0311] In some demonstrative aspects, AAC controller 102 may be configured to extract a plurality of residual noise acoustic modes from residual noise input 106 , eg, including a plurality of disjoint residual noise acoustic modes that are statistically independent.

[0312] For example, the controller 193 may include an extractor (also referred to as an "acoustic pattern extractor" or a "feature extractor") for extracting a plurality of reference acoustic patterns and / or a plurality of residual noise acoustic patterns.

[0313] As used herein, the phrase "disjoint acoustic modes" may refer to multiple acoustic modes that are independent with respect to at least one characteristic and / or property (e.g., energy, amplitude, phase, frequency, direction, one or more statistical signal characteristics, etc.).

[0314] In some demonstrative aspects, controller 193 may extract a plurality of reference acoustic patterns by applying a predefined reference noise extraction function to plurality of reference noise inputs 104 .

[0315] In some illustrative aspects, the extraction of the plurality of acoustic modes may be used, for example, to model a dominant mode of input 104 as a combination of a predefined number of, for example, disjoint acoustic modes corresponding to a corresponding number of, for example, disjoint modeled sound sources.

[0316] In one example, it may be assumed that one or more expected noise patterns expected to affect the sound control zone 110 may be generated by one or more of road noise, wind noise, engine noise, etc. Therefore, the controller 193 may be configured to select one or more reference acoustic patterns based on one or more properties of the road noise pattern, the wind noise pattern, the engine noise pattern, and / or any other noise pattern.

[0317] In some demonstrative aspects, controller 193 may extract the plurality of residual noise acoustic patterns by applying a predefined residual noise extraction function to plurality of residual noise inputs 106 .

[0318] In some demonstrative aspects, AAC controller 102 may be configured to determine acoustic mode extractor settings of the acoustic mode extractor based on AAC information 129 and, for example, determine a sound control mode of sound control signal 109 based on the acoustic mode extractor settings, eg, as described below.

[0319] In some demonstrative aspects, acoustic mode extractor settings may include one or more acoustic mode extractor coefficients applied by the acoustic mode extractor to determine a plurality of reference acoustic modes and / or a plurality of residual noise acoustic modes, eg, as described below.

[0320] In some illustrative aspects, the controller 193 can be configured to determine, update and / or adjust the setting of at least one acoustic pattern extractor parameter based on the AAC information 129, for example in real time, and determine the sound control mode of the sound control signal 109, for example, based on the acoustic pattern extractor parameter settings, for example, as described below.

[0321] In some illustrative aspects, acoustic mode extractor parameter settings may include settings of one or more coefficients, one or more weight parameters, and / or any other parameters that may be utilized by the acoustic mode extractor to extract multiple non-intersecting reference acoustic modes and / or multiple non-intersecting residual noise acoustic modes.

[0322] In some illustrative aspects, AAC information 129 may include passenger tracking information to indicate the position of a passenger's head and / or ears.

[0323] For example, the information source 120 may include a camera, an image sensor, an optical sensor, and / or any other sensor that may be configured to track the position of the passenger's head and / or ears. For example, the AAC controller 102 may be configured to determine and / or set one or more AAC parameters, such as predictive filter settings, path transfer function settings, and / or acoustic mode extractor settings, for example, based on the passenger tracking information.

[0324] In one example, the AAC controller 102 can be configured to set one or more AAC parameters, such as predictive filter settings, path transfer function settings, and / or acoustic pattern extractor settings (e.g., in real time) based on changes in the position of a passenger's head and / or ears within the sound control zone 110 (e.g., in real time).

[0325] In one example, the AAC controller 102 can be configured to set path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more residual noise sensing locations 107, path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more noise sensing locations 105, and / or path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more monitoring locations 103, for example (e.g., in real time) based on changes in the position of a passenger's head and / or ears within the sound control zone 110.

[0326] In some illustrative aspects, AAC information 129 may include seat position information corresponding to the positioning of one or more seats in the vehicle. For example, AAC information 129 may include seat position information corresponding to the positioning of a driver's seat and / or the positioning of one or more passenger seats in the vehicle.

[0327] In one example, the AAC controller 102 may be configured to set one or more AAC parameters, such as predictive filter settings, path transfer function settings, and / or acoustic pattern extractor settings, for example, based on the seat position information (eg, in real time).

[0328] In one example, the AAC controller 102 can be configured to set path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more residual noise sensing locations 107, path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more noise sensing locations 105, and / or path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more monitoring locations 103, for example (e.g., in real time) based on changes in the seat position of the driver and / or passenger (e.g., in real time).

[0329] In some illustrative aspects, AAC information 129 may include passenger information corresponding to one or more passengers in the vehicle. For example, AAC information 129 may include passenger information to indicate the count, position, location, size, and / or measurements of one or more passengers in the vehicle.

[0330] In one example, the AAC controller 102 may be configured to set one or more AAC parameters, such as predictive filter settings, path transfer function settings, and / or acoustic pattern extractor settings, based on passenger information (eg, in real time), for example (eg, in real time).

[0331] In one example, the AAC controller 102 can be configured to set path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more residual noise sensing locations 107, path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more noise sensing locations 105, path transfer function settings for a path transfer function between the acoustic transducer 108 and one or more monitoring locations 103, acoustic mode extractor settings, and / or predictive filter settings, for example (e.g., in real time) based on a count, position, location, size, and / or measurement of one or more passengers in the vehicle.

[0332] In some demonstrative aspects, AAC information 129 may include climate information corresponding to the climate within the vehicle.

[0333] In one example, the AAC controller 102 can be configured to set one or more AAC parameters, such as predictive filter settings, path transfer function settings, and / or acoustic mode extractor settings, based on changes in the in-vehicle climate (e.g., in real time), for example.

[0334] In one example, the AAC controller 102 can be configured to set path transfer function settings for path transfer between the acoustic transducer 108 and one or more residual noise sensing locations 107, path transfer function settings for path transfer between the acoustic transducer 108 and one or more noise sensing locations 105, path transfer function settings for path transfer functions between the acoustic transducer 108 and one or more monitoring locations 103, acoustic mode extractor settings, and / or predictive filter settings, for example, based on changes in climate in the vehicle (e.g., in real time). For example, the AAC controller 102 can be configured to set path transfer function settings for path transfer between the acoustic transducer 108 and one or more residual noise sensing locations 107, path transfer function settings for path transfer between the acoustic transducer 108 and one or more noise sensing locations 105, path transfer function settings for path transfer functions between the acoustic transducer 108 and one or more monitoring locations 103, acoustic mode extractor settings, and / or predictive filter settings, for example, based on detected changes in temperature and / or humidity levels in the vehicle indicated by the AAC information 129 (e.g., in real time).

[0335] In some illustrative aspects, AAC information 129 may include vehicle system information corresponding to a noise-generating vehicle system of the vehicle, and AAC controller 102 may be configured to determine a sound control mode of sound control signal 109 based on the vehicle system information, for example, as described below.

[0336] In some illustrative aspects, AAC controller 102 may be configured to determine a sound control mode for sound control signal 109 , for example based on vehicle system information, such that the sound control mode will be used to reduce or eliminate noise from noise-generating vehicle systems within sound control zone 110 , for example, as described below.

[0337] In some illustrative aspects, vehicle systems that generate noise may include, for example, a vehicle engine, a vehicle tires, a vehicle braking system, a vehicle steering system, a vehicle air conditioning system, and / or any other system of the vehicle.

[0338] In some demonstrative aspects, AAC information 129 may include vehicle system settings information representing settings of vehicle systems of the vehicle, eg, as described below.

[0339] In some illustrative aspects, AAC controller 102 may be configured to determine the sound control mode based on vehicle system setting information, eg, as described below.

[0340] In some illustrative aspects, AAC controller 102 may be configured to determine a first sound control mode for sound control signal 109 , for example based on AAC information 129 including first vehicle system setting information representing a first setting for a vehicle system, eg, as described below.

[0341] In some illustrative aspects, the AAC controller 102 can be configured to determine a second sound control mode of the sound control signal 109 that is different from the first sound control mode, for example based on AAC information 129 including second vehicle system setting information representing a second setting of the vehicle system that is different from the first setting of the vehicle system, for example, as described below.

[0342] In some illustrative aspects, AAC controller 102 may be configured to determine and / or set a sound control mode for sound control signal 109 , for example, based on changes in vehicle system setting information indicating changes in settings of a vehicle system, eg, as described below.

[0343] In some demonstrative aspects, AAC information 129 may include operating mode information representative of operating modes of vehicle systems of the vehicle, eg, as described below.

[0344] In some demonstrative aspects, AAC controller 102 may be configured to determine a sound control mode for sound control signal 109 , eg, based on operating mode information, eg, as described below.

[0345] In some illustrative aspects, AAC controller 102 may be configured to determine a first sound control mode for sound control signal 109 , for example based on AAC information 129 including first operating mode information representative of a first operating mode of a vehicle system, eg, as described below.

[0346] In some illustrative aspects, the AAC controller 102 can be configured to determine a second sound control mode of the sound control signal 109 that is different from the first sound control mode, for example based on the AAC information 129 including second operating mode information representing a second operating mode of the vehicle system that is different from the first operating mode of the vehicle system, for example, as described below.

[0347] In some illustrative aspects, AAC controller 102 may be configured to set the sound control mode of sound control signal 109 based on a change in operating mode information representative of a change in an operating mode of a vehicle system, for example, as described below.

[0348] In some demonstrative aspects, AAC controller 102 may be configured to determine a sound control profile based on AAC information 129 and, for example, determine a sound control mode of sound control signal 109 based on the sound control profile, eg, as described below.

[0349] In some illustrative aspects, the sound control profile may include settings of one or more sound control parameters, and AAC controller 102 may be configured to determine a sound control mode of sound control signal 109 based on the settings of the one or more sound control parameters, e.g., as described below.

[0350] In some illustrative aspects, memory 198 may be configured to store a plurality of sound control profiles (AAC profiles) 199 that respectively correspond to a plurality of sound control configurations, e.g., as described below.

[0351] In some illustrative aspects, an AAC profile 199 corresponding to a particular sound control configuration may include, for example, settings of one or more AAC parameters, such as prediction filter settings, path transfer function settings, and / or acoustic pattern extractor settings corresponding to the particular sound control configuration, e.g., as described below.

[0352] In some illustrative aspects, AAC controller 102 may be configured to select a selected sound control profile from multiple sound control profiles 198 based on AAC information 129 and determine a sound control mode based on the selected sound control profile, e.g., as described below.

[0353] In some illustrative aspects, the controller 193 can be configured to determine a sound control mode of the sound control signal 109 based on the AAC information 129, for example, such that the sound control mode will be used to control, reshape, reduce, or eliminate noise from one or more noise sources in at least one sound control zone 110, for example, as described below.

[0354] In one example, the AAC information 129 may include RPM information of the vehicle's engine.

[0355] In one example, the controller 193 can be configured to determine a sound control mode of the sound control signal 109 based on RPM information, for example, such that the sound control mode will be used to reduce or eliminate engine noise and / or modify the sound control mode for improving reduction of other noise sources within at least one sound control zone 110.

[0356] In another example, the controller 193 can be configured to determine and / or modify the sound control mode of the sound control signal 109 based on the RPM information, for example, based on any other additional or alternative criteria, for example, to support control and / or reduction of one or more other sound modes, for example, to support reduction and / or elimination of noise from one or more other noise sources.

[0357] In another example, the controller 193 may be configured to selectively and / or dynamically turn on / off, mute, and / or slow down and / or stop (freeze) one or more AAC functions, e.g., based on the AAC information 129 , e.g., as described below.

[0358] In yet another example, the controller 193 may be configured to selectively and / or dynamically turn on / off, mute and / or slow down and / or stop (freeze) one or more AAC functions, for example, based on RPM information and / or any other type of information in the AAC information 129, for example, as described below.

[0359] In another example, the AAC information 129 may include window / roof information to indicate the open / closed state of the vehicle's windows and / or roof, and / or the roof type of the roof, such as a metal roof or a panoramic roof. For example, the controller 193 may be configured to determine a sound control mode of the sound control signal 109, for example based on the window / roof information, such that the sound control mode is used to reduce or eliminate external noise from the environment of the vehicle, such as wind noise and road noise, etc., within at least one sound control zone 110.

[0360] In yet another example, the AAC information 129 may include road detection information corresponding to a road detection system of the vehicle. For example, the controller 193 may be configured to determine a sound control mode of the sound control signal 109, for example based on the road detection information, such that the sound control mode is used to reduce or eliminate external noise from the environment of the vehicle within at least one sound control zone 110, for example based on the road type indicated by the road detection information.

[0361] In another example, the AAC information 129 may include tire information corresponding to a tire system of the vehicle. For example, the controller 193 may be configured to determine a sound control mode of the sound control signal 109, for example based on the tire information, such that the sound control mode will reduce or eliminate noise from the tires within the at least one sound control zone 110, for example based on the pressure of one or more tires of the vehicle and / or the type and / or size of one or more tires of the vehicle.

[0362] In yet another example, the AAC information 129 may include climate information corresponding to the climate outside the vehicle. For example, the controller 193 may be configured to determine a sound control mode of the sound control signal 109, for example based on the climate information, such that the sound control mode is used to reduce or eliminate external noise from the environment of the vehicle, such as rain noise, wind noise, road noise, and / or any other noise, within at least one sound control zone 110.

[0363] In another example, the AAC information 129 may include steering information corresponding to a steering system of the vehicle. For example, the controller 193 may be configured to determine a sound control mode of the sound control signal 109, for example based on the steering information, such that the sound control mode will be used to reduce or eliminate external noise from the environment of the vehicle within at least one sound control zone 110, for example based on the angle of the vehicle steering wheel (e.g., left / right steering angle).

[0364] In yet another example, the AAC information 129 may include brake system information to indicate an operating state of a primary brake system, an emergency brake system, an anti-lock brake system (ABS), and / or any other brake system of the vehicle. For example, the controller 193 may be configured to determine a sound control mode of the sound control signal 109, for example based on the brake system information, such that the sound control mode reduces or eliminates external noise from the environment of the vehicle within at least one sound control zone 110, for example based on the operating state of the brake system.

[0365] In some illustrative aspects, AAC controller 193 may be configured to determine and / or set (e.g., in real time) a sound control mode to be provided to acoustic transducer 108, e.g., via sound control signal 109, e.g., based on AAC information 129, e.g., as described below.

[0366] In some illustrative aspects, the AAC controller 193 can be configured to determine and / or set (e.g., in real time) a sound control mode to be provided to the acoustic transducer 108, e.g., via the noise control signal 109, for example, by selectively generating the noise control signal 109 and / or selectively providing the noise control signal 109 to the acoustic transducer 108, e.g., as described below.

[0367] In some illustrative aspects, the AAC controller 193 can be configured to determine and / or set (e.g., in real time) a sound control mode to be provided to the acoustic transducer 108, e.g., via the sound control signal 109, for example, by selecting whether to provide the sound control signal 109 to the acoustic transducer 108, e.g., as described below.

[0368] In some illustrative aspects, the AAC controller 193 can be configured to, for example, based on the AAC information 129, for example, dynamically and / or selectively (e.g., in real time) mute a sound control mode to be provided to the acoustic transducer 108, for example, via the sound control signal 109, and / or dynamically reduce the level of a sound control mode to be provided to the acoustic transducer 108, for example, via the sound control signal 109, for example, as described below.

[0369] In some illustrative aspects, the AAC controller 193 can be configured to dynamically identify one or more predefined situations (“silent situations”) based on the AAC information 129, for example, in real time, wherein, for example, a sound control mode provided to the acoustic transducer 108 is muted or set to a reduced level via the sound control signal 109, for example, as described below.

[0370] In some illustrative aspects, AAC controller 193 may be configured to mute or reduce the level of a sound control pattern provided to acoustic transducer 108 , e.g., based on identification of a predefined silence condition, e.g., via sound control signal 109 , e.g., as described below.

[0371] In some demonstrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , eg, via sound control signal 109 , such as by setting a predictive filter (PF) to zero, eg, as described below.

[0372] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , eg, via sound control signal 109 , such as by setting an input to reference sensor 104 to zero, eg, as described below.

[0373] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , eg, via sound control signal 109 , such as by setting sound control signal 109 to zero, eg, as described below.

[0374] In some illustrative aspects, AAC controller 193 may be configured to generate a sound control pattern, such as via sound control signal 109 , such as by selecting not to invoke an AAC function, such that the sound control pattern provided to acoustic transducer 108 is muted, e.g., as described below.

[0375] In some illustrative aspects, AAC controller 193 may be configured to mute the sound control pattern provided to acoustic transducer 108, e.g., via sound control signal 109, e.g., by selectively zeroing some or all of the inputs / outputs of the acoustic mode extractor, e.g., as described below.

[0376] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , for example, based on any other additional or alternative settings and / or mechanisms.

[0377] In some demonstrative aspects, AAC information 129 may include speech detection information to indicate detected speech of one or more occupants of the vehicle.

[0378] In some demonstrative aspects, information source 120 may include a speech detector to generate speech detection information.

[0379] In one example, the speech detector may be configured to generate speech detection information, for example, based on acoustic information from the reference acoustic sensor 104 and / or the monitoring acoustic sensor.

[0380] In another example, the speech detector can be configured to generate speech detection information, for example, based on acoustic information from one or more other acoustic sensors (e.g., a dedicated speech detection sensor and / or any other dedicated or non-dedicated sensor).

[0381] In some demonstrative aspects, AAC controller 193 may be configured to mute a sound control mode provided to acoustic transducer 108 , such as via sound control signal 109 , based on recognizing that AAC information 129 indicates that speech is detected.

[0382] In some demonstrative aspects, AAC information 129 may include audio information corresponding to audio heard in the vehicle.

[0383] In some illustrative aspects, information source 120 may include an audio source or audio controller to provide and / or control audio heard in the vehicle.

[0384] In some demonstrative aspects, AAC controller 193 may be configured to selectively set one or more AAC parameters used to generate sound control signal 109 , eg, based on the audio information.

[0385] In some illustrative aspects, AAC controller 193 may be configured to selectively set one or more AAC parameters used to generate sound control signal 109 , for example, based on an audio level and / or an equalization level of audio to be heard in a vehicle.

[0386] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108, e.g., based on an output level of acoustic transducer 108, e.g., via sound control signal 109. For example, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108, e.g., based on detecting that an output level of acoustic transducer 108 is greater than a predefined threshold (“maximum speaker threshold”), and / or based on detecting that an output level of acoustic transducer 108 is less than a predefined threshold (“minimum speaker threshold”), e.g., via sound control signal 109.

[0387] In some illustrative aspects, AAC controller 193 may be configured to, for example, based on the level of noise input 104, such as via sound control signal 109, mute a sound control pattern provided to acoustic transducer 108. For example, AAC controller 193 may be configured to, for example, based on detecting that the level of noise input 104 is greater than a predetermined threshold (a "maximum reference threshold") and / or based on detecting that the level of noise input 104 is less than a predetermined threshold (a "minimum reference threshold"), such as via sound control signal 109, mute a sound control pattern provided to acoustic transducer 108.

[0388] In some illustrative aspects, AAC controller 193 may be configured to, for example, based on the level of residual noise input 106, e.g., via sound control signal 109, mute a sound control pattern provided to acoustic transducer 108. For example, AAC controller 193 may be configured to, for example, based on detecting that the level of residual noise input 106 is greater than a predetermined threshold (a "maximum residual threshold") and / or based on detecting that the level of residual noise input 106 is less than a predetermined threshold (a "minimum residual threshold"), e.g., via sound control signal 109, mute a sound control pattern provided to acoustic transducer 108.

[0389] In some illustrative aspects, AAC controller 193 may be configured to, for example, mute a sound control pattern provided to acoustic transducer 108 based on a level of monitoring input corresponding to monitoring location 103, e.g., via sound control signal 109. For example, AAC controller 193 may be configured to, for example, mute a sound control pattern provided to acoustic transducer 108 based on detection of a monitoring level greater than a predefined threshold (a "maximum monitoring threshold"), and / or based on detection of a monitoring input level less than a predefined threshold (a "minimum monitoring threshold"), e.g., via sound control signal 109.

[0390] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via noise control signal 109 , based on a determination that one or more acoustic sensors are faulty and / or malfunctioning.

[0391] In some illustrative aspects, AAC controller 193 may be configured to detect a malfunction and / or failure of one or more acoustic sensors, eg, based on AAC information 129 .

[0392] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , eg, via sound control signal 109 , eg, based on a determination that one or more reference noise sensors 119 are faulty and / or malfunctioning.

[0393] In some illustrative aspects, AAC controller 193 may be configured to detect, for example based on noise input 104 and / or based on any other information in AAC information 129 , that one or more reference acoustic sensors 119 are malfunctioning and / or failing.

[0394] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , eg, via sound control signal 109 , eg, based on a determination that one or more residual noise acoustic sensors 121 are faulty and / or malfunctioning.

[0395] In some illustrative aspects, AAC controller 193 may be configured to detect, for example based on residual noise input 106 and / or based on any other information in AAC information 129 , that one or more residual noise acoustic sensors 121 are malfunctioning and / or failing.

[0396] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , based on speed information corresponding to a speed of a vehicle including sound control zone 110 .

[0397] In one example, the AAC controller 193 can be configured to mute a sound control mode provided to the acoustic transducer 108, for example via the sound control signal 109, based on detecting that speed information indicates that the vehicle's speed is above a predefined vehicle speed threshold and / or outside a predefined vehicle speed range.

[0398] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , based on opening status information corresponding to one or more openings of the vehicle that include sound control zone 110 .

[0399] In one example, the AAC controller 193 can be configured to mute a sound control mode provided to the acoustic transducer 108, for example, based on detecting opening status information indicating that a door of the vehicle is open, a window is open (e.g., exceeds a predefined opening percentage), a trunk of the vehicle is open, and / or a roof of the vehicle is open (e.g., exceeds a predefined opening percentage), for example via a sound control signal 109.

[0400] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , based on tire information corresponding to a tire system of a vehicle including sound control zone 110 .

[0401] In one example, the AAC controller 193 may be configured to mute a sound control pattern provided to the acoustic transducer 108 , for example via the sound control signal 109 , based on detecting that tire information indicates that the tire pressure of one or more tires is not within a predefined tire pressure range.

[0402] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , based on climate information corresponding to a climate in a vehicle including sound control zone 110 .

[0403] In one example, the AAC controller 193 can be configured to mute a sound control mode provided to the acoustic transducer 108, for example, via a sound control signal 109, based on detecting climate information indicating that the temperature in the vehicle is not within a predefined temperature range, and / or the humidity level in the vehicle is not within a predefined humidity level range.

[0404] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , based on climate information corresponding to a climate outside the vehicle including sound control zone 110 .

[0405] In one example, the AAC controller 193 can be configured to mute a sound control mode provided to the acoustic transducer 108, for example, via a sound control signal 109, based on detecting climate information indicating that the temperature outside the vehicle is not within a predefined temperature range, and / or the humidity level outside the vehicle is not within a predefined humidity level range.

[0406] In some illustrative aspects, AAC controller 193 may be configured to mute a sound control pattern provided to acoustic transducer 108 , such as via sound control signal 109 , based on vehicle system information corresponding to a vehicle system of the vehicle including sound control zone 110 .

[0407] In one example, the AAC controller 193 may be configured to mute a sound control mode provided to the acoustic transducer 108 , for example via the sound control signal 109 , based on detecting that vehicle system information indicates that the operating condition of the vehicle system is not within a predefined operating condition range.

[0408] In one example, the AAC controller 193 may be configured to mute a sound control pattern provided to the acoustic transducer 108 , such as via the sound control signal 109 , based on detecting vehicle system information indicating that the engine RPM is not within a predefined RPM range.

[0409] In one example, the AAC controller 193 can be configured to mute the sound control mode provided to the acoustic transducer 108, for example, via a sound control signal 109, based on detecting vehicle system information indicating that the operating condition of the vehicle's air conditioning system is not within a predefined operating condition range and / or the blower speed of the vehicle's air conditioning system is not within a predefined blower operating condition range.

[0410] In some demonstrative aspects, controller 193 may be configured to set a sound control mode of sound control signal 109 , eg, based on a change in AAC information 129 that indicates a change in an acoustic configuration of operation of the AAC system, eg, as described below.

[0411] For example, the controller 193 may be configured to monitor (eg, dynamically and / or continuously monitor) the AAC input 129 to detect changes to the AAC information 129 (eg, in real time).

[0412] For example, the controller 193 may be configured to update (eg, dynamically and / or continuously) a sound control mode of the sound control signal 109 , eg, based on detected changes in the AAC information 129 (eg, in real time).

[0413] In some demonstrative aspects, AAC system 100 may include a neural network-based (NN-based) AAC system, e.g., as described below.

[0414] In some illustrative aspects, a NN-based AAC system may, for example, utilize one or more NNs to estimate and / or determine one or more parameters, outputs, functions, etc. that may be used to determine, set, and / or generate a sound control signal 109, e.g., as described below.

[0415] In some demonstrative aspects, AAC controller 102 may include NN 150 that may be trained to generate NN output 159 based on NN input including AAC configuration information 129 , e.g., as described below.

[0416] In some illustrative aspects, NN 150 may include a deep NN (DNN), e.g., as described below. In other aspects, any other NN type, architecture, and / or topology may be used.

[0417] In some demonstrative aspects, AAC controller 102 may be configured to generate sound control signal 109 , for example, based on NN output 159 of NN 150 , eg, as described below.

[0418] In some illustrative aspects, NN 150 may be trained to generate a sound control pattern for controlling sounds within sound control zone 110, for example, based on AAC information 129, e.g., as described below.

[0419] In some illustrative aspects, NN 150 may be trained to generate a sound control pattern for controlling sounds within sound control zone 110, eg, based on AAC information 129 and / or plurality of noise inputs 104, eg, as described below.

[0420] In some demonstrative aspects, NN 150 may be trained to generate NN output 159 based on NN inputs including AAC configuration information 129 and multiple noise inputs 104, e.g., as described below.

[0421] In some illustrative aspects, NN 150 may be trained to generate a sound control pattern for controlling sounds within sound control zone 110 based on, for example, AAC information 129 , plurality of noise inputs 104 , and optionally plurality of residual noise inputs 106 , for example, as described below.

[0422] In some illustrative aspects, NN 150 may be trained to generate a generated sound control pattern for controlling sounds within sound control zone 110, for example, based on acoustic information corresponding to a plurality of acoustic sensing locations, e.g., as described below.

[0423] In some demonstrative aspects, the plurality of acoustic sensing locations may include one or more physical sensing locations of one or more physical acoustic sensors, eg, as described below.

[0424] In some demonstrative aspects, the plurality of acoustic sensing locations may include one or more virtual sensing locations of one or more virtual acoustic sensors, eg, as described below.

[0425] In some demonstrative aspects, the plurality of acoustic sensing locations may include one or more noise sensing locations, eg, as described below.

[0426] In some demonstrative aspects, the plurality of acoustic sensing locations may include one or more residual noise sensing locations, eg, as described below.

[0427] In some demonstrative aspects, the plurality of acoustic sensing locations may include one or more monitoring sensing locations, eg, as described below.

[0428] In some illustrative aspects, AAC controller 102 may be configured to apply pre-calibrated noise cancellation to the sound control pattern generated by NN 150. For example, the pre-calibrated noise cancellation may be based on a filter calibration, such as an optimal filter calibration, to, for example, reduce noise control zone 110 ( Figure 2 ) in the noise.

[0429] In some illustrative aspects, NN 150 can be trained to generate a sound control pattern for controlling sounds within sound control zone 110 based on AAC information 129, for example, multiple noise inputs 104 corresponding to noise sensing locations 105 and / or monitoring locations 103, and / or multiple residual noise inputs 106 corresponding to residual noise sensing locations 105, for example, as described below.

[0430] In other aspects, the NN 150 can be trained to generate a NN output 159 including settings for one or more sound control parameters, which can be used to generate a sound control pattern for controlling sounds within the sound control zone 110, for example, as described below.

[0431] In some illustrative aspects, NN 150 may be trained to generate NN outputs including settings of one or more PF parameters and / or transducer transfer function (TTF) parameters (e.g., speaker transfer function (STF) parameters), e.g., based on AAC information 129, e.g., as described below.

[0432] Some illustrative aspects are described herein with respect to STFs and / or STF parameters (eg, corresponding to a loudspeaker). These aspects may be implemented with respect to any other TTFs and / or TTF parameters, eg, corresponding to any other transducer.

[0433] In some demonstrative aspects, controller 193 may be configured to generate sound control signal 109 , for example, based on settings of one or more PF parameters and / or TTF parameters, such as STF parameters, for example, as described below.

[0434] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, may be implemented to provide a technical solution for improving ANC performance, e.g., under various conditions, while supporting the use of a reduced, e.g., minimal, set of measured transfer functions (TFs), e.g., as described below.

[0435] In some demonstrative aspects, a NN-based AAC system, such as AAC system 100 , may be implemented to provide a technical solution for enabling fast adaptation of new ANC configurations, such as by leveraging an existing database.

[0436] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, may be implemented to provide a technical solution for reducing, e.g., minimizing the number of required calibration tests of the NN-based AAC system, e.g., by extrapolating an estimated transfer function from a minimum set of measured TFs.

[0437] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, may be implemented to provide a technical solution that can be adapted for implementation with respect to general vehicle cabin configurations, e.g., seat implementations, and / or that can improve the NN-based AAC system, e.g., through ongoing training.

[0438] In some illustrative aspects, a NN-based AAC system, for example, AAC system 100 may be implemented to provide a technical solution for extracting performance from a nonlinear portion of a TF using an activation function, for example, compared to other AAC systems that may perform only on a linear portion of the TF between a reference microphone 119 and an error microphone 121.

[0439] In some illustrative aspects, an NN-based AAC system, such as AAC system 100, can provide a technical solution for supporting robustness in dynamic scenarios. For example, an NN-based AAC system can be trained with multiple scenarios, such as different PFs for each different scenario (e.g., different road types, temperatures, seat positions, etc.) that may be required compared to other AAC systems, for example, without even increasing the size and / or complexity of the NN-based AAC system, such as in terms of memory and / or space.

[0440] In some illustrative aspects, a NN-based AAC system, such as AAC system 100, may be implemented to provide a technique for utilizing multiple parameters that may be linearly related to multiple input features and / or parameters, such as in AAC information 129. For example, an AAC system that implements such a linear correlation may be advantageous over other AAC systems that may have a non-linear correlation, such as an exponential correlation, with the number of input features and / or parameters.

[0441] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, may be implemented to provide a technical solution that can be used in a real-time environment, e.g., a real-time vehicle environment and / or any other environment, such as for determining one or more parameters and / or settings of the AAC system 100, e.g., as described below.

[0442] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, may be implemented to provide a technical solution for a dynamic MTF and / or a dynamic TTF (e.g., STF) that may be configured to be suitable for an AAC system, e.g., as described below.

[0443] In some illustrative aspects, a NN-based AAC system (e.g., AAC system 100) can be implemented, for example, based on AAC information 129 to provide a technical solution for a neural network architecture that can span an acoustic space based on input parameters and / or measured parameters as its axes.

[0444] In one example, NN 150 can be configured to utilize NN inputs including AAC information 129, a NN outer layer including an impulse response of a noise source, and / or a NN objective function including, for example, a mean squared error of an error microphone in, for example, a target region 110, e.g., as described below.

[0445] In other aspects, any other NN architecture, NN outer layer and / or NN objective function may be utilized.

[0446] In some illustrative aspects, NN 150 can be trained, for example, in a training / learning phase, for example, to determine acoustic control signal 109, for example, as described below.

[0447] In some illustrative aspects, NN 150 can be trained, such as in a training / learning phase, e.g., to determine one or more parameters and / or functions that can be used to determine voice control signal 109, e.g., as described below.

[0448] In some illustrative aspects, NN 150 may be trained, for example, in a training / learning phase, for example, to determine one or more parameters of a PF, which may be used for sound control signal 109, for example, as described below.

[0449] In some illustrative aspects, NN 150 may be trained, for example, in a training / learning phase, for example, to determine one or more parameters of a TTF (eg, STF) of a transducer, such as transducer 108, for example, as described below.

[0450] In some illustrative aspects, NN 150 may be trained, for example, in a training / learning phase, to, for example, determine a microphone transfer function of an error microphone, such as error sensor 121 , as described below.

[0451] In some illustrative aspects, NN 150 may be trained, for example, in a training / learning phase, to determine microphone transfer functions of monitoring microphones, for example, at monitoring locations 103, as described below.

[0452] In some illustrative aspects, NN 150 can be trained, e.g., in a training / learning phase, e.g., to determine any other additional or alternative parameters, functions, and / or settings that can be used to determine sound control signal 109, e.g., as described below.

[0453] In some illustrative aspects, NN 150 may be trained, for example, based on reinforcement learning, such as using real-time training, e.g., as described below.

[0454] In one example, real-time training using reinforcement learning may be used, for example, to determine when to update a sound control pattern of the sound control signal 109 , for example, as described below.

[0455] In another example, for example, real-time training using reinforcement learning can be used to provide a technical solution that can be configured for production calibration and / or operation. For example, real-time training using reinforcement learning can be used for one or more predictions, and / or updating NN 150.

[0456] In some demonstrative aspects, a NN-based AAC system, e.g., AAC system 100 , may be trained, configured, and / or updated during one or more phases, e.g., as described below.

[0457] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100 , may be trained, configured, and / or updated during a development phase, e.g., as described below.

[0458] In one example, a NN-based AAC system can be trained, for example during a development phase, to determine one or more MTFs and / or TTFs (e.g., STFs), parameters, and / or settings, for example based on one or more predefined and / or recorded signals of, for example, a vehicle setup (e.g., assuming a constant TTF (e.g., STF)).

[0459] In one example, a NN-based AAC system may be tuned, for example using reinforcement learning, such as for fine-tuning the NN-based AAC system during operation, e.g., during live and / or real-time operation.

[0460] In some illustrative aspects, the configuration of a NN of a NN-based AAC system, e.g., NN 150, can be set to a production NN, e.g., “frozen”, for example, for production, after fine-tuning.

[0461] In some demonstrative aspects, a NN-based AAC system, e.g., AAC system 100 , may be trained, configured, and / or updated during a production phase, e.g., as described below.

[0462] In one example, a frozen NN configuration may be used as an initial NN for an ANC system, such as during a production phase. For example, an ANC system, such as ANC system 100, may be configured with an initial NN setup, such as that which may include or may be based on, for example, a copy of a post-fine-tuned "frozen" NN.

[0463] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, can be trained, configured, and / or updated, e.g., by a customer, during real-time use. In one example, the weights of NN 150 can be slowly updated, e.g., using reinforcement learning. For example, the weights of NN 150 can be updated to compensate for changes in one or more TFs, e.g., deformation, speaker degradation, etc., during the lifetime of an installed NN-based AA system.

[0464] In some demonstrative aspects, training NN 150 may be trained, for example, based on one or more AAC parameters corresponding to NN-based AAC system 100 , eg, as described below.

[0465] In some demonstrative aspects, the AAC parameters used to train NN 150 may include one or more of the parameters described above with respect to AAC information 129 .

[0466] In one example, AAC parameters used to train NN 150 for a vehicle deployment of AAC system 100 may include, for example, one or more of seat position, seat occupancy, rear seat recline, number of people in the vehicle, road type, speed, climate information, and / or any other additional or alternative parameters.

[0467] In some illustrative aspects, the NN-based AAC system, for example, AAC system 100, can be implemented to provide a system for providing an AAC system that can be configured to support AAC with respect to multiple acoustic state scenarios, for example, to mitigate noise control zones, for example, sound control zones 110 ( Figure 2 ) in the technical solution of the noise, for example, as described below.

[0468] In one example, an AAC system may be configured to generate a noise cancellation pattern for a given acoustic state. However, in some cases and / or scenarios, such as in real-time implementations, it may be desirable to provide support for, for example, generating noise cancellation patterns with relatively high accuracy, such as even with respect to multiple acoustic state scenarios (e.g., multiple acoustic state scenarios in which one or more acoustic settings may change).

[0469] In some illustrative aspects, a NN-based AAC system (e.g., AAC system 100) may be implemented, for example, based on a relatively large amount of measured data to provide a technical solution for utilizing a trainable NN, such as NN 150. For example, in a vehicle implementation, a relatively large amount of measurements corresponding to the acoustic configuration in the vehicle cabin may be readily accessed.

[0470] In some illustrative aspects, changes in measured data corresponding to an acoustic configuration of an AAC system may be quantified into a final number of filters in a feasible and / or relatively easy manner, e.g., to support training of a NN-based AAC system, e.g., AAC system 100 .

[0471] In some illustrative aspects, a NN-based AAC system, e.g., AAC system 100, can be configured to provide a technical solution for supporting rapid integration of one or more sensing capabilities used as AAC auxiliary information, e.g., information 129, into a NN, e.g., NN 150. In one example, the sensing capabilities can include, for example, input from a camera, CAN data, and / or any other sensor, such as information from one or more sensors in the vehicle cabin.

[0472] refer to Figure 3, which schematically illustrates a training scheme 300 for training a NN-based AAC system according to some illustrative aspects.

[0473] In some illustrative aspects, NN 150 may be trained according to one or more (e.g., some or all) of the operations of training scheme 300. Figure 1 )

[0474] In some illustrative aspects, such as Figure 3 As shown, NN 350 can be trained to generate sound control patterns 355, for example, as described below.

[0475] In some illustrative aspects, such as Figure 3 As shown, NN 350 may be trained to generate sound control pattern 355 , for example, based on AAC information 329 , for example, CAN data in a vehicle, plurality of residual noise inputs 306 , and / or plurality of noise inputs 304 .

[0476] In some illustrative aspects, such as Figure 3 As shown, the loss function 360 may be determined, for example, based on the sound control mode 355 and the predetermined sound control mode 365. For example, the predetermined sound control mode 365 may include a pre-calibrated noise cancellation mode, which may be set during the production and / or calibration phase of the NN-based ANC system, for example, as described above.

[0477] In some illustrative aspects, loss function 360 may be determined, for example, based on a difference between sound control pattern 355 and predetermined sound control pattern 365, for example, based on subtracting predetermined sound control pattern 365 from sound control pattern 355. For example, a calibration error for training NN 350 may be included and / or may be represented.

[0478] Return to reference Figure 1 In some illustrative aspects, controller 193 may be configured to process input information 195 to determine a sound control mode for controlling sound within sound control zone 110, for example, based on plurality of noise inputs 104, eg, as described below.

[0479] In some demonstrative aspects, NN 150 may be trained to generate NN output 159 based on NN input based on AAC configuration information 129, e.g., as described below.

[0480] In some illustrative aspects, controller 193 may be configured to generate a sound control pattern for controlling acoustic transducer 108 , for example, based on NN output 159 , eg, as described below.

[0481] In some demonstrative aspects, input 191 may be configured to receive AAC configuration information 129 , eg, via a system bus of a vehicle including sound control zone 110 , eg, as described above.

[0482] In some illustrative aspects, input 191 may be configured to receive AAC configuration information 129, for example, via at least one of: CAN bus information received via a CAN bus of the vehicle, A2B bus information received via an A2B bus of the vehicle, MOST bus information received via a MOST bus of the vehicle, wireless communication information received via a wireless communication link, or Ethernet bus information received via an Ethernet bus of the vehicle, e.g., as described above. In other aspects, input 191 may be configured to receive AAC configuration information 129 via any other additional or alternative input interface, network interface, and / or bus interface.

[0483] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129, which includes information corresponding to the configuration of AAC within sound control zone 110, e.g., as described below.

[0484] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes information representing the spectral distribution of acoustic signals in at least one of sound control zone 110 and / or an environment of sound control zone 110, e.g., as described below.

[0485] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129, which includes information representing one or more parameters that affect real-time configuration of AAC within sound control zone 110, e.g., as described below.

[0486] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes information representative of one or more physical characteristics of sound control zone 110, e.g., as described below.

[0487] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes information representing one or more acoustic characteristics of sound control zone 110, e.g., as described below.

[0488] In some illustrative aspects, the NN 150 can be trained to generate a NN output 159 based on AAC configuration information 129 that includes information from one or more information sources 120 that are different from one or more acoustic sensors of the AAC system 100, such as acoustic sensors at a noise sensing location 105, a residual noise sensing location 107, and / or a monitoring location 103, for example, as described below.

[0489] In some illustrative aspects, the NN 150 can be trained to generate a NN output 159 based on AAC configuration information 129 that includes information from one or more information sources 120 that are independent of one or more acoustic sensors of the AAC system 100, such as acoustic sensors at a noise sensing location 105, a residual noise sensing location 107, and / or a monitoring location 103, e.g., as described below.

[0490] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes vehicle speed information corresponding to a speed of a vehicle that includes sound control zone 110 , e.g., as described below.

[0491] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes engine information corresponding to an engine of a vehicle that includes sound control zone 110 , e.g., as described below.

[0492] In some illustrative aspects, the NN 150 can be trained to generate a NN output 159 based on AAC configuration information 129, which includes at least one of braking system information, road detection information, steering information, tire information, seat information, vehicle type information, and / or opening state information, for example, as described below.

[0493] For example, the brake system information may include information corresponding to a brake system of a vehicle including the sound control zone 110 , eg, as described above.

[0494] For example, the road detection information may include information from a road detection system of a vehicle including the sound control zone 110 , eg, as described above.

[0495] For example, the steering information may include information corresponding to a steering system of a vehicle including the voice control zone 110 , eg, as described above.

[0496] For example, the tire information may include information corresponding to one or more tires of a vehicle including the sound control zone 110, eg, as described above.

[0497] For example, the seat information may include information corresponding to at least one of the position, e.g., location and / or orientation, e.g., angle, and / or occupancy of one or more seats of a vehicle including the sound control zone 110, e.g., as described above.

[0498] For example, the vehicle type information may include information corresponding to the type of vehicle that includes the sound control zone 110 , eg, as described above.

[0499] For example, the opening state information may include information corresponding to the opening state of a vehicle including the sound control zone 110 , eg, as described above.

[0500] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes passenger information corresponding to one or more passengers of a vehicle that includes voice control zone 110 , e.g., as described below.

[0501] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes audio system information corresponding to an audio system of a vehicle that includes sound control zone 110 , e.g., as described below.

[0502] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes climate information corresponding to at least one of a climate within sound control zone 110 or a climate outside sound control zone 110, e.g., as described below.

[0503] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes user position information corresponding to a position of at least one of a user's head or ears within sound control zone 110, e.g., as described below.

[0504] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes user identity information corresponding to a user identity to control user preferences regarding sound control zone 110, e.g., as described below.

[0505] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 including vehicle system configuration information corresponding to configuration of operating modes of one or more vehicle systems of a vehicle including sound control zone 110, e.g., as described below.

[0506] In some illustrative aspects, NN 150 may be trained to generate NN output 159 based on AAC configuration information 129 that includes vehicle sensor information from one or more vehicle sensors of a vehicle that includes sound control zone 110 , e.g., as described below.

[0507] In some demonstrative aspects, NN 150 may be trained to generate NN output 159, for example, based on NN inputs including AAC configuration information 129 and noise information based on multiple noise inputs 104, e.g., as described below.

[0508] In some demonstrative aspects, NN 150 may be trained to generate NN output 159 including, for example, settings of one or more AAC parameters, e.g., as described below.

[0509] In some demonstrative aspects, controller 193 may be configured to generate a sound control pattern for controlling acoustic transducer 108 , eg, based on settings of one or more AAC parameters, eg, as described below.

[0510] In some illustrative aspects, the setting of one or more AAC parameters may include the setting of at least one of a prediction filter and / or a path transfer function, e.g., as described below. In other aspects, the setting of one or more AAC parameters may include the setting of any other additional or alternative parameters.

[0511] In some demonstrative aspects, NN 150 may include a prediction filter (PF) NN 152 that may be trained to generate NN output 159 including PF settings, e.g., based on NN inputs, e.g., as described below.

[0512] In some demonstrative aspects, controller 193 may be configured to generate a sound control pattern for controlling acoustic transducer 108, for example, by applying a PF to noise information that is configured according to a PF setting determined by PF NN 152, for example, as described below.

[0513] In some demonstrative aspects, the noise information may be based on, for example, number of noise inputs 104 , eg, as described below.

[0514] In some illustrative aspects, the controller 195 can be configured to generate a sound control pattern for controlling the acoustic transducer 108, for example, based on input information 195 including noise error information (e.g., residual noise input 106) representing noise errors at one or more error sensing locations, for example, as described below.

[0515] In some demonstrative aspects, controller 193 may be configured to determine, for example, based on noise error information 106 , noise information to which a PF is to be applied, eg, as described below.

[0516] In some demonstrative aspects, PF NN 152 may be trained to generate NN output 159 including PF settings that may include, for example, a plurality of PF coefficients, e.g., as described below.

[0517] In some demonstrative aspects, PF NN 152 may be trained to generate NN output 159 including a PF setting that may include, for example, a weight vector representing a plurality of coefficients, e.g., as described below.

[0518] In other aspects, the PF NN 152 may be trained to generate a NN output 159 that includes other additional or alternative information to define the PF settings.

[0519] In some demonstrative aspects, controller 193 may include a parameter extractor configured to determine extracted AAC parameter information, eg, based on sensor information from one or more sensors, eg, as described below.

[0520] In some demonstrative aspects, NN 150 may be trained to generate NN output 159 based on NN input including extracted AAC parameter information, e.g., as described below.

[0521] In some illustrative aspects, the parameter extractor may include a principal component analysis (PCA) extractor, which may be configured, for example, to determine extracted AAC parameter information based on PCA of sensor information and / or based on PCA of AAC information 129, for example, in the time domain or the frequency domain, for example, as described below.

[0522] In some demonstrative aspects, controller 193 may include a parameter extractor that may be configured to determine extracted AAC parameter information based on acoustic sensor information from one or more acoustic sensors, for example, as described below.

[0523] In some demonstrative aspects, the PCA extractor may be configured to determine extracted AAC parameter information, eg, based on PCA of noise sensor information from one or more noise sensors, eg, as described below.

[0524] In some demonstrative aspects, the PCA extractor may be configured to determine extracted AAC parameter information, eg, based on PCA of residual noise sensor information from one or more residual noise sensors, eg, as described below.

[0525] In some demonstrative aspects, the PCA extractor may be configured to determine extracted AAC parameter information, for example, based on PCA of monitoring sensor information from one or more monitoring sensors, eg, as described below.

[0526] In some demonstrative aspects, the PCA extractor may be configured to determine extracted AAC parameter information, for example, based on PCA of virtual sensor information from one or more virtual sensors, eg, as described below.

[0527] In some illustrative aspects, the PCA extractor may be configured to determine extracted AAC parameter information including, for example, at least one of: road type information, cabin state information, passenger position information, vehicle system state information, noise characteristic information, and / or noise state information, e.g., as described below. In other aspects, the PCA extractor may be configured to determine extracted AAC parameter information including any other additional or alternative types of information.

[0528] For example, the road type information may include information corresponding to the type of road on which the vehicle including the sound control zone 110 is traveling.

[0529] For example, the cabin state information may include information corresponding to a state of a cabin of a vehicle including the sound control zone 110 .

[0530] For example, the passenger position information may include information corresponding to the position of one or more passengers in a cabin of a vehicle including the sound control zone 110 .

[0531] For example, the vehicle system status information may include information corresponding to a status of a system of the vehicle including the sound control zone 110 .

[0532] For example, the noise characteristic information may include information representative of one or more characteristics of the noise to be mitigated.

[0533] In one example, the noise characteristic information may include information related to the frequency band of the noise to be mitigated; the level of noise to be mitigated; the type of noise to be mitigated; the source of the noise to be mitigated; for example, one or more main peaks of the noise to be mitigated relative to one or more other nearby peaks or all range frequencies, for example, peaks at ~200 Hz>10db, peaks above 100-400 Hz or 30-1000 Hz energy levels, etc.; and / or the level that increases over time at a specific frequency of the noise to be mitigated, etc.

[0534] For example, the noise state information may include information corresponding to the state of noise to be mitigated.

[0535] In one example, the noise status information may include an indication of whether the noise to be mitigated includes transient noise or non-transient noise, etc.

[0536] In some illustrative aspects, controller 193 may be configured to train PF NN 152, for example, based on noise errors at one or more error sensing locations, such as one or more physical and / or virtual residual noise sensing locations 107, e.g., as described below.

[0537] In some illustrative aspects, controller 193 may be configured to configure PF NN 152 , for example, based on a transducer transfer function (TTF) setting of TTFs between plurality of acoustic transducers 108 and one or more acoustic sensing locations, eg, as described below.

[0538] In some illustrative aspects, the one or more acoustic sensing locations may include physical sensing locations of physical acoustic sensors. For example, controller 193 may be configured to configure PF NN 152, for example, based on a TTF setting of a TTF between acoustic transducer 108 and an acoustic sensor physically located at the acoustic sensing location, e.g., as described below.

[0539] In some illustrative aspects, the one or more acoustic sensing locations may include a virtual sensing location of a virtual acoustic sensor. For example, controller 193 may be configured to configure PF NN 152, for example, based on a TTF setting of a TTF between acoustic transducer 108 at the virtual acoustic sensing location and the virtual acoustic sensor, e.g., as described below.

[0540] In some illustrative aspects, the one or more acoustic sensing locations may include a residual noise sensing location within sound control zone 110. For example, controller 193 may be configured to configure PF NN 152, for example, based on a TTF setting of a TTF between acoustic transducer 108 and residual noise sensing location 107.

[0541] In some illustrative aspects, the one or more acoustic sensing locations may include a monitoring sensing location.For example, controller 193 may be configured to configure PF NN 152 , for example, based on a TTF setting of a TTF between acoustic transducer 108 and monitoring sensing location 103 .

[0542] In some illustrative aspects, the one or more acoustic sensing locations may include a noise sensing location outside of sound control zone 110. For example, controller 193 may be configured to configure PF NN 152, for example, based on a TTF setting of a TTF between acoustic transducer 108 and noise sensing location 105.

[0543] In some demonstrative aspects, controller 193 may include TTF NN 154, which may be trained to determine TTF settings, for example, based on AAC configuration information 129, e.g., as described below.

[0544] In some demonstrative aspects, controller 193 may be configured to train TTF NN 154, for example, based on sensed acoustic information corresponding to one or more acoustically sensed locations, eg, as described below.

[0545] For example, the sensed acoustic information corresponding to one or more acoustic sensing locations may include acoustic information sensed by physical acoustic sensors at physical acoustic sensing locations.

[0546] For example, the sensed acoustic information corresponding to the one or more acoustic sensing locations may include virtual acoustic information sensed by a virtual acoustic sensor at the virtual acoustic sensing locations.

[0547] In some illustrative aspects, the virtual acoustic sensor denoted as e at the virtual residual noise sensing position denoted as Acoustic information denoted as p may be sensed by a physical acoustic sensor denoted as P at a physical sensing position, for example, as described below.

[0548] In some illustrative aspects, for example, in response to a sound control pattern denoted s output by an acoustic transducer denoted S and a noise signal denoted R from a noise source sensed, for example, by a reference (noise) acoustic sensor denoted r located at a noise sensing location, the acoustic information of the virtual acoustic sensor e It may be determined, for example, based on acoustic information sensed by a physical acoustic sensor P, for example, as described below.

[0549] In some illustrative aspects, a signal denoted Pmtf representing a noise signal from a noise source sensed by a physical acoustic sensor P may be determined, for example, by subtracting the effect of a sound control mode s from the acoustic information p, such as as follows:

[0550] P MTF =p(sensed by microphone P)-s*STF p

[0551] Among them STF p represents a transfer function between the acoustic transducer S and the physical acoustic sensor P, such as a fine-tuned and / or real-time transfer function.

[0552] In some illustrative aspects, acoustic information of the virtual acoustic sensor e can be determined, for example, based on the signal Pmtf For example:

[0553]

[0554] in represents the transfer function between the physical acoustic sensor P and the virtual acoustic sensor e, for example, a pre-calibrated transfer function when the acoustic transducer S is not activated, and

[0555] in represents the transfer function between the reference sensor r and the virtual acoustic sensor e, such as a pre-calibrated transfer function.

[0556] In some illustrative aspects, a PF setting (eg, a PF setting of PF NN 152) may be determined, for example, according to a criterion, which may be based on estimated acoustic information of a virtual acoustic sensor e.

[0557] In one example, the PF setting (eg, the PF setting of the PF NN 152) may be determined, for example, based on the following minimization criteria:

[0558]

[0559] in represents a transfer function between the acoustic transducer S and the virtual acoustic sensor e, such as a pre-calibrated transfer function.

[0560] In another example, the PF setting (eg, the PF setting of the PF NN 152) may be determined, for example, based on the following minimization criteria:

[0561]

[0562] in represents the transfer function between the physical acoustic sensor P and the virtual acoustic sensor e, for example a pre-calibrated transfer function when the acoustic transducer S is active, and

[0563] Among them STF p represents a transfer function between the acoustic transducer S and the physical acoustic sensor P, such as a fine-tuned and / or real-time transfer function.

[0564] In another example, the PF setting (eg, the PF setting of the PF NN 152) may be determined, for example, based on the following minimization criteria:

[0565]

[0566] in represents a transfer function between the acoustic transducer S and the physical acoustic sensor P, for example a pre-calibrated transfer function.

[0567] In some demonstrative aspects, controller 193 may include a parameter extractor, which may be configured to determine extracted AAC parameter information, eg, based on the AAC configuration information, eg, as described below.

[0568] In some demonstrative aspects, controller 193 may be configured to provide NN input including extracted AAC parameter information to NN 150, e.g., as described below.

[0569] In some demonstrative aspects, controller 193 may be configured to provide NN 150 with a NN input that includes noise error information representative of noise errors at one or more error sensing locations, e.g., as described below.

[0570] In some demonstrative aspects, controller 193 may be configured to provide NN input to NN 150 based on noise error information, e.g., as described below.

[0571] In some illustrative aspects, controller 193 may be configured to determine settings for one or more sound control parameters by configuring input to NN 150 based on AAC information 129, and determine a sound control mode based on the settings for the one or more sound control parameters, e.g., as described below.

[0572] In other aspects, the controller 193 may be configured to determine settings for one or more sound control parameters based on any other additional or alternative criteria related to the AAC information 129 .

[0573] In some demonstrative aspects, the settings of the one or more sound control parameters may include a PF setting for determining a sound control mode, for example, based on number of noise inputs 104 and / or number of residual noise inputs 106 , eg, as described below.

[0574] In some illustrative aspects, the settings of the one or more sound control parameters may include a plurality of predictive filter coefficients to configure a PF to be applied to determine the sound control mode based on the plurality of noise inputs 104 and / or the plurality of residual noise inputs 106 , e.g., as described below.

[0575] In some demonstrative aspects, the settings of the one or more sound control parameters may include a predictive filter weight vector to be applied to determine a sound control mode based on number of noise inputs 104 and / or number of residual noise inputs 106 , eg, as described below.

[0576] In some illustrative aspects, the settings of the one or more sound control parameters may include one or more path transfer functions to be applied to determine a sound control mode based on multiple noise inputs 104 and / or multiple residual noise inputs 106, for example, including one or more TTFs, such as speaker transfer functions (STFs), for example, as described below.

[0577] In other aspects, the settings of the one or more sound control parameters may include settings of one or more additional or alternative parameters, weights, coefficients and / or functions to be applied to determine the sound control mode based on the multiple noise inputs 104 and / or the multiple residual noise inputs 106.

[0578] In some demonstrative aspects, controller 193 may determine sound control signal 109 , for example, by applying an estimation function and / or a prediction function to noise input 104 and / or residual noise input 106 , eg, as described below.

[0579] In some demonstrative aspects, controller 193 may include an estimator (also referred to as a “prediction unit”) configured to apply an estimation or prediction function to noise input 104 and / or residual noise input 106 , eg, as described below.

[0580] In some demonstrative aspects, controller 193 may be configured to cause an estimator or prediction unit to utilize one or more prediction parameters, eg, based on AAC information 129 , eg, for use in an estimation function, eg, as described below.

[0581] In one example, the controller 193 may be configured to determine a first set of prediction parameters for a first AAC configuration of the AAC system 100 , for example based on the first AAC information 129 .

[0582] In another example, the controller 193 may be configured to determine a second set of prediction parameters for a second AAC configuration of the AAC system 100 , eg, based on the second AAC information 129 .

[0583] In some illustrative aspects, the prediction parameters may include weights, coefficients, functions, and / or any other additional or alternative parameters to be used in determining the sound control mode, e.g., as described below.

[0584] In some demonstrative aspects, the predicted parameters may include one or more path transfer function parameters of an estimated or predicted function, eg, as described below.

[0585] In one example, the prediction parameters may include one or more TTFs (e.g., STFs) to be applied by the controller 193 to determine the sound control mode. In one example, the TTFs (e.g., STFs) may include a representation of an acoustic path from one or more of the acoustic transducers 108 to one or more of the noise sensing locations 105, the monitoring location 103, and / or the residual noise sensing location 107.

[0586] In other aspects, the prediction parameters can include any other additional or alternative parameters.

[0587] In some demonstrative aspects, controller 193 may be configured to determine and / or set one or more of the TTFs (eg, STFs) based on AAC information 129 , eg, as described below.

[0588] In some demonstrative aspects, controller 193 may be configured to determine and / or set one or more of the prediction parameters based on AAC information 129 , eg, as described below.

[0589] In some illustrative aspects, NN 150 may be trained to generate NN output 159 of NN 150 that includes settings for one or more AAC parameters, and controller 193 may be configured to generate a sound control signal based on the settings for one or more AAC parameters, e.g., as described below.

[0590] In some illustrative aspects, the NN 150 can be trained to generate a NN output 159 that includes settings for a prediction filter, settings for a path transfer function (e.g., TTF or STF), and / or settings for any other parameter that can be used to determine, generate, configure, and / or set a sound control signal 109 to control the acoustic transducer 108, e.g., as described below.

[0591] In some demonstrative aspects, NN 150 may be trained to generate NN output 159 to configure settings of a prediction filter, for example, based on AAC information 129 .

[0592] In some demonstrative aspects, PF NN 152 may be trained to generate prediction filter settings for at least one prediction filter based on AAC information 129 , eg, as described below.

[0593] In some illustrative aspects, PF NN 152 may be trained to model changes in an interior cabin of a vehicle, for example, in an improved manner.

[0594] In some illustrative aspects, the PF NN 152 can be implemented to provide predictive filter settings, for example, to provide a technical solution for generating a sound control signal 109 with reduced computational complexity, for example, compared to an NN-based AAC system that utilizes a NN trained to directly output an acoustic sound control pattern configured to control sounds within the sound control zone 110.

[0595] In one example, it may be assumed that changes to the PF settings may be made relatively slowly, and therefore, the workload on the PF NN 152 may be relatively low, e.g., in real-time operation. For example, the AAC controller 193 may be configured to sample the PF NN 152, e.g., every few cycles, and overwrite the existing PF settings with the updated PF settings output by the PF NN 152.

[0596] In some demonstrative aspects, prediction filter NN 152 may be trained to output a plurality of PF coefficients, eg, based on AAC information 129 , eg, in the form of a prediction filter weight vector, eg, as described below.

[0597] In some demonstrative aspects, controller 193 may be configured to determine sound control signal 109 , for example, by applying a PF according to prediction filter coefficients determined by PF NN 152 to plurality of reference noise inputs 104 , eg, as described below.

[0598] In some illustrative aspects, the prediction filter NN 152 can be trained to generate predictive filter settings for the prediction filter based on, for example, a path transfer function (e.g., TTF (e.g., STF)) between the acoustic transducer 108 and an acoustic sensing location (e.g., the residual noise sensing location 107, the monitoring location 103, and / or the noise sensing location 105), for example, as described below.

[0599] In some illustrative aspects, prediction filter NN 152 may be trained to generate prediction filter settings for a prediction filter based on a path transfer function (eg, TTF (eg, STF)) between acoustic transducer 108 and residual noise sensing location 107 , for example, as described below.

[0600] In some demonstrative aspects, NN 150 may include STF NN 154 trained to generate TTF settings (eg, STF settings) corresponding to, for example, a path transfer function between acoustic transducer 108 and residual noise sensing location 107 , for example, as described below.

[0601] In some illustrative aspects, predictive filter NN 152 may be trained to generate predictive filter settings for a predictive filter, for example, based on a path transfer function (eg, TTF (eg, STF)) between acoustic transducer 108 and noise sensing location 105 , for example, as described below.

[0602] In some illustrative aspects, TTF (STF) NN 154 may be trained to generate TTF settings (eg, STF settings) corresponding to, for example, a path transfer function between acoustic transducer 108 and residual noise sensing location 105 , for example, as described below.

[0603] In some illustrative aspects, predictive filter NN 152 may be trained to generate predictive filter settings for a predictive filter, for example, based on a path transfer function (eg, TTF (eg, STF)) between acoustic transducer 108 and monitoring location 103 , for example, as described below.

[0604] In some demonstrative aspects, NN 150 may include STF NN 154 that is trained to generate TTF settings (eg, STF settings) corresponding to, for example, a path transfer function between acoustic transducer 108 and monitoring location 103, for example, as described below.

[0605] In some demonstrative aspects, TTF (STF) NN 154 may be trained to generate TTF settings (eg, STF settings) of a path transfer function, for example, based on AAC information 129 , eg, as described below.

[0606] In some illustrative aspects, the AAC controller 102 may include an acoustic feedback (AFB) mitigator (also referred to as an "AFB controller," "AFB canceller," "feedback canceller (FBC)," "echo mitigator," or "echo canceller") that may be configured to mitigate AFB between the acoustic transducer 108 and one or more acoustic sensors of the AAC system 100 (e.g., one or more of the reference noise acoustic sensor 119 and / or the residual noise sensor 121), for example, as described below.

[0607] In one example, the AFB mitigator may be configured to mitigate AFB between the one or more acoustic transducers 108 and the one or more reference noise acoustic sensors 119 , eg, as described below.

[0608] In another example, the AFB mitigator may be configured to mitigate AFB between the one or more acoustic transducers 108 and the one or more residual noise sensors 121 , eg, as described below.

[0609] In some illustrative aspects, controller 193 may be configured to generate an AFB mitigation signal, for example, by applying an AFB setting to an acoustic transducer signal, such as acoustic transducer signal 109 to be provided to one or more acoustic transducers 108 , for example, as described below.

[0610] In some demonstrative aspects, an AFB setting may include a plurality of AFB mitigation coefficients to be applied to an acoustic transducer signal to, for example, generate an AFB mitigation signal.

[0611] In some demonstrative aspects, controller 193 may be configured to determine a sound control mode to apply to transducer 108m, for example, based on the AFB mitigation signal, eg, as described below.

[0612] In some illustrative aspects, controller 193 may be configured to determine the AFB mitigation signal corresponding to the particular acoustic sensor signal, for example, by subtracting the AFB mitigation signal and the AFB mitigation signal corresponding to the particular acoustic sensor signal from the particular acoustic sensor signal, eg, as described below.

[0613] In one example, the controller 193 may be configured to determine an AFB mitigation signal corresponding to the noise input 106 from the reference noise sensor 119 , such as by applying AFB settings corresponding to the reference noise sensor 119 and the acoustic transducer 108 to the acoustic transducer signal 109 .

[0614] For example, the controller 193 may be configured to determine an AFB mitigation signal corresponding to the noise input 106 from the reference noise sensor 119 , such as by subtracting the AFB mitigation signal corresponding to the noise input 106 from the noise input 106 .

[0615] In another example, the controller 193 may be configured to determine an AFB mitigation signal corresponding to the residual noise input 104 from the residual noise sensor 121 , such as by applying AFB settings corresponding to the residual noise sensor 121 and the acoustic transducer 108 to the acoustic transducer signal 109 .

[0616] For example, the controller 193 may be configured to determine an AFB mitigation signal corresponding to the residual noise input 104 from the residual noise sensor 121 , such as by subtracting the AFB mitigation signal corresponding to the residual noise input 104 from the residual noise input 104 .

[0617] In some demonstrative aspects, one or more operations and / or functions of an AFB mitigator may be implemented by an AFB mitigation NN, eg, as described below.

[0618] In some demonstrative aspects, NN 150 may include an AFB mitigation NN 155 that may be trained to generate NN outputs 159 including AFB settings, for example, based on NN inputs, e.g., as described below.

[0619] In some demonstrative aspects, the AFB settings may include a plurality of AFB coefficients to be applied to an acoustic transducer signal (eg, acoustic transducer signal 109 to be provided to one or more acoustic transducers 108 ), for example, as described below.

[0620] In some illustrative aspects, the AFB NN 155 can be configured to provide a technical solution for adapting the AFB settings to changes in the acoustic medium, for example, between an acoustic transducer of the AAC system 100 (e.g., the acoustic transducer 108) and an acoustic sensor of the AAC system 100 (e.g., the reference noise sensor 119 and / or the residual noise sensor 121), as described below.

[0621] In some demonstrative aspects, AFB NN 155 may be trained to generate AFB settings, for example, based on AAC configuration information 129 , eg, as described below.

[0622] In some demonstrative aspects, controller 193 may be configured to determine an AAC profile based on AAC information 129 , eg, as described below.

[0623] In some demonstrative aspects, controller 193 may be configured to determine a sound control mode for sound control signal 109 based on an AAC profile, eg, as described below.

[0624] In some demonstrative aspects, controller 193 may be configured to set one or more parameters and / or properties of NN 150 based on an AAC profile, for example, as described below.

[0625] In some demonstrative aspects, an AAC profile may include settings for one or more sound control parameters that may be used to determine a sound control mode of sound control signal 109 , eg, as described below.

[0626] In some illustrative aspects, controller 193 may be configured to determine a sound control mode for sound control signal 109 , for example, based on settings of one or more sound control parameters, eg, as described below.

[0627] In some demonstrative aspects, memory 198 may be configured to store a plurality of AAC profiles 199 , eg, as described below.

[0628] In some demonstrative aspects, AAC configuration file 199 may include settings of one or more sound control parameters corresponding to an AAC operating configuration of AAC system 100 , eg, as described below.

[0629] In one example, first AAC profile 199 may correspond to a first AAC operating configuration of AAC system 100. According to this example, first AAC profile 199 corresponding to the first AAC operating configuration of AAC system 100 may include, for example, first settings of one or more sound control parameters. For example, the first settings of one or more sound control parameters may be configured for sound control applied, for example, when AAC system 100 operates in a first operating situation.

[0630] In another example, second AAC profile 199 may correspond to a second AAC operating configuration of AAC system 100. According to this example, second AAC profile 199 corresponding to the second AAC operating configuration of AAC system 100 may include, for example, second settings of one or more sound control parameters, e.g., different from the first settings. For example, the second settings of one or more sound control parameters may be configured for sound control applied, e.g., when AAC system 100 operates in a second operating situation, e.g., different from the first operating situation.

[0631] In some illustrative aspects, controller 193 may be configured to select a selected AAC profile from multiple AAC profiles 199, e.g., based on AAC information 129, and determine a sound control mode of sound control signal 109, e.g., based on the selected AAC profile, e.g., as described below.

[0632] In some demonstrative aspects, AAC profiles 199 may include one or more user-based profiles corresponding to one or more users, eg, as described below.

[0633] In some demonstrative aspects, a user-based profile corresponding to a user may include settings for one or more sound control parameters based on user preferences, for example, as described below.

[0634] In some illustrative aspects, a user-based profile may correspond to a user and may allow the user to control user preferences regarding sound control zone 110 , eg, as described above.

[0635] In one example, the user-based profile may correspond to a user of the sound control zone 110. For example, a user-based profile for a vehicle driver may include settings for one or more sound control parameters based on the driver's preferences regarding the sound control zone 110 implemented relative to the vehicle driver's seat.

[0636] In another example, a user-based profile may correspond to a first user to control user preferences regarding a sound control zone 110 that may be used by a second user. For example, a user-based profile for a vehicle driver may include settings for one or more sound control parameters based on the driver's preferences regarding sound control zones 110 implemented relative to one or more passenger seats of the vehicle.

[0637] In some demonstrative aspects, AAC information 129 may include user identity information corresponding to a user identity, and controller 193 may select a selected user-based profile from among a plurality of AAC profiles 199 based on the user identity information.

[0638] In one example, the AAC profile 199 may include a user-based profile corresponding to a vehicle driver. For example, the controller 193 may be configured to identify identity information corresponding to the vehicle driver, for example based on AAC information 129 received, for example, from a system of the vehicle. For example, the controller 193 may select a selected user-based profile corresponding to the driver from a plurality of AAC profiles 199, for example based on user identity information corresponding to the driver.

[0639] For example, a user-based profile corresponding to a driver may include information defining one or more voice control parameter settings for voice control zone 110 based on the driver's preferences.

[0640] In one example, a user-based profile corresponding to a driver may include information defining one or more voice control parameter settings for a driver voice control zone 110 corresponding to a driver seat. In another example, a user-based profile corresponding to a driver may include information defining one or more voice control parameter settings for a passenger voice control zone 110 corresponding to a passenger seat in the vehicle.

[0641] In some illustrative aspects, controller 193 may be configured to determine a sound control mode for sound control signal 109 corresponding to sound control zone 110 , for example, based on settings of one or more sound control parameters of sound control zone 110 , e.g., according to a user-based profile corresponding to a driver.

[0642] refer to Figure 4 , which schematically illustrates a NN-based AAC system 400 , according to some illustrative aspects.

[0643] For example, the AAC system 100 ( Figure 1 ) may include one or more elements of AAC system 400 and / or may perform one or more operations and / or functions of AAC system 400.

[0644] In some illustrative aspects, such as Figure 4 As shown, the AAC system 400 may include a PF NN 452 , such as a PFD NN, trained to generate PF settings for a prediction unit 460 .

[0645] For example, PF NN 152( Figure 1 ) may include one or more elements of PF NN 452 and / or may perform one or more operations and / or functions of PFNN 452.

[0646] In some illustrative aspects, such as Figure 4 As shown, the prediction unit 460 may be configured to predict and / or estimate an estimation function or prediction function 463 to be applied to one or more noise inputs 404 to, for example, generate a sound control pattern 409, for example, as described below.

[0647] In some illustrative aspects, such as Figure 4 As shown, the noise input 404 may be provided by one or more acoustic sensors 419 and may represent acoustic noise from at least one noise source 417. For example, the acoustic sensor 419 may include the noise sensor 119 ( Figure 1 ) and / or may implement one or more elements of the noise sensor 119 ( Figure 1 )'s one or more operations and / or functions.

[0648] In some illustrative aspects, such as Figure 4 As shown, the sound control mode 409 can be configured to control at least one acoustic transducer 408. For example, the acoustic transducer 408 can include one or more acoustic transducers 108 ( Figure 1 ) and / or may perform one or more acoustic transducers 108 ( Figure 1 )'s one or more operations and / or functions.

[0649] In some illustrative aspects, such as Figure 4 As shown, the PF NN 452 can be configured to generate PF settings for the prediction unit 460 based on, for example, NN inputs, which can include AAC information 429 from one or more information sources 420 (e.g., environmental sources and / or any other sources). For example, the AAC information 429 can include information from the information source 120 ( Figure 1 ) of AAC information 129( Figure 1 ).

[0650] In some demonstrative aspects, PF NN 452 may be trained to generate PF settings for prediction unit 460 based on AAC information 429 , for example.

[0651] In some demonstrative aspects, noise input 404 and AAC information 429 may be used as inputs to NN-based AAC system 400 .

[0652] In some illustrative aspects, the output of AAC system 400 may include, for example, sound control mode 409 , which may be configured to mitigate noise from noise source 417 , for example, at error sensing location 421 .

[0653] In some demonstrative aspects, PF NN 452 may be trained and / or calibrated, for example, based on noise errors, eg, as described below.

[0654] In some illustrative aspects, the noise error at the error sensing location 407 may be based on, for example, a comparison of an acoustic signal at the error sensing location 407, represented as Y, and an acoustic signal at the error sensing location 407, represented as The differences between the acoustic sound control modes.

[0655] In some illustrative aspects, the acoustic sound control mode at the error sensing location 407 The error sensing location 407 may be based on the sound control pattern 409 and the TTF (STF) 411 between the acoustic transducer 408 and the error sensing location 407 .

[0656] In some demonstrative aspects, acoustic signal Y at error sensing location 407 may be based on, for example, impulse response 415 of a vocal channel between noise source 417 and error sensing location 407 .

[0657] In some illustrative aspects, error sensing location 407 may include a sound control zone, such as sound control zone 110 ( Figure 2 ) in the error sensing position.

[0658] In one example, the noise error at the error sensing location 407 can be detected by one or more residual noise sensors, such as the residual noise sensor 121 ( Figure 1 ) sensing. For example, a monitoring microphone, such as the error microphone 121 ( Figure 1 ) to sense, for example, noise errors that may be caused by acoustic changes of the NN-based AAC system 400 (e.g., acoustic changes in a vehicle cabin implementing the NN-based AAC system 400).

[0659] In another example, the noise error at the error sensing location 407 may be a virtual residual noise virtually sensed by, for example, a virtual sensor at the error sensing location 407. For example, the noise error at the error sensing location 407 may be virtually sensed by, for example, a virtual sensor. Figure 1 )) is used to determine the noise error at the error sensing location 407 based on the noise sensed by the monitoring sensor at the error sensing location.

[0660] In some illustrative aspects, PF NN 452 may be trained, for example, based on error signal 457 , which may correspond to a noise error at error sensing location 407 .

[0661] In some illustrative aspects, residual noise sensor 421, e.g., an error microphone, can be configured to generate error signal 457, e.g., during training of PFNN 452. In other aspects, error signal 457 can be determined based on a virtual acoustic signal of a virtual acoustic sensor at error sensing location 407, e.g., as described above.

[0662] In some illustrative aspects, for example, during training of PF NN 452 , predefined noise pattern 417 may be generated, predefined sets of AAC information 429 may be provided as NN inputs to PF NN 452 , and error signal 457 may be determined.

[0663] In some illustrative aspects, for example, during training of PF NN 452 , determination of error signal 457 may be repeated for, for example, a plurality of different predefined noise patterns 417 and / or a plurality of different predefined settings of AAC information 429 .

[0664] In some illustrative aspects, for example, during training of PF NN 452 , PF NN 452 may be trained, for example, based on a training criterion of minimizing or eliminating error signal 457 .

[0665] refer to Figure 5 , which schematically illustrates a NN-based PF including a PF NN 552 to configure a setting of a prediction unit 560 according to some illustrative aspects.

[0666] For example, PF NN 452( Figure 4 ) may include one or more elements of PF NN 552 and / or may perform one or more operations and / or functions of PFNN 552; and / or prediction unit 460 ( Figure 4 ) may include one or more elements of the prediction unit 562 and / or may perform one or more operations and / or functions of the prediction unit 560.

[0667] In some demonstrative aspects, PF NN 552 may be trained to generate NN output 503 that includes PF settings for prediction unit 560 .

[0668] For example, the PF NN 552 may be trained to generate a NN output 503 that includes a plurality of PF coefficients of a prediction function to be applied by the prediction unit 560. For example, the PF NN 552 may be trained to generate a NN output 503 that includes a weight vector representing the plurality of PF coefficients.

[0669] In some illustrative aspects, such as Figure 5 As shown, the prediction unit 560 may be configured to predict and / or estimate the prediction function 513 to be applied to the noise input 504 , for example, to generate the sound control pattern 509 .

[0670] In some illustrative aspects, such as Figure 5 As shown, PF NN 552 may be configured to generate PF settings for prediction unit 560 based on NN input 501, for example, as described below.

[0671] In some illustrative aspects, NN input 501 can be configured to receive AAC information 529 from, for example, one or more information sources. For example, AAC information 529 can include information from information source 120 ( Figure 1 ) of AAC information 129( Figure 1 ).

[0672] In some demonstrative aspects, AAC information 529 may include one or more vehicle / cabin parameters corresponding to settings in a vehicle cabin, for example, in a vehicle AAC implementation, eg, as described above.

[0673] In one example, the AAC information 529 may include seat position information (“Seat Position”) corresponding to the positioning of a driver's seat and / or the positioning of one or more passenger seats in the vehicle cabin, for example, as described above.

[0674] In another example, the AAC information 529 may include temperature information corresponding to the temperature inside the cabin and / or the temperature outside the cabin, eg, as described above.

[0675] In other aspects, AAC information 529 may include any other additional or alternative information, eg, as described above.

[0676] In some demonstrative aspects, PF NN 552 may be trained to generate NN output 513 including PF settings for prediction unit 560 , for example, based on AAC information 529 .

[0677] In some illustrative aspects, such as Figure 5 As shown, the PF NN 552 may include a plurality of layers including an input layer 551 , a plurality of hidden layers 553 , and an output layer 557 .

[0678] In some illustrative aspects, such as Figure 5 As shown, multiple layers of the PF NN 552 may be connected via multiple edges / nodes.

[0679] In some illustrative aspects, such as Figure 5 As shown, input layer 551 may be configured to receive AAC information 529 .

[0680] In some illustrative aspects, such as Figure 5 As shown, the output layer 557 may be configured to provide a PF setting to a prediction unit 560 , for example, based on the AAC information 529 .

[0681] In some illustrative aspects, such as Figure 5 As shown, it can be determined, for example, by applying the prediction function 503 to the noise input 504, represented as Acoustic sound control mode.

[0682] refer to Figure 6 , which schematically illustrates PF NN 652 according to some illustrative aspects.

[0683] For example, PF NN 452( Figure 4 ) and / or PF NN 552( Figure 5 ) may include one or more elements of PF NN 652 and / or may perform one or more operations and / or functions of PF NN 652.

[0684] In some illustrative aspects, PF NN 652 may be trained to generate a prediction unit, such as prediction unit 550 ( Figure 5 )'s PF sets the NN output of 603.

[0685] In some demonstrative aspects, PF NN 652 may be trained to generate NN outputs including PF settings 603, e.g., to configure a prediction unit to predict and / or estimate a prediction function to be applied to noise input 604 (e.g., from one or more reference microphones).

[0686] In some illustrative aspects, such as Figure 6 As shown, PF NN 652 may be configured to receive, for example, NN input including AAC information 629 from one or more AAC information sources. For example, AAC information 629 may include information from information source 120 ( Figure 1 ) of AAC information 129( Figure 1 ).

[0687] In one example, if Figure 6 As shown, the AAC information 629 may include information corresponding to one or more physical parameters.

[0688] For example, Figure 6 As shown, the AAC information 629 may include pressure information, denoted as P, corresponding to the pressure in the vehicle cabin.

[0689] For example, Figure 6 As shown, the AAC information 629 may include chair distance information, denoted as D, corresponding to the arrangement of one or more chairs in the vehicle cabin.

[0690] For example, Figure 6 As shown, the AAC information 629 may include speed information represented as V corresponding to the vehicle speed.

[0691] For example, Figure 6 As shown, the AAC information 629 may include temperature information, denoted as T, corresponding to the temperature in the vehicle cabin.

[0692] In other aspects, AAC information 629 may include any other additional or alternative AAC configuration information, eg, as described above.

[0693] In some demonstrative aspects, PF NN 652 may be trained to generate PF settings 603 for prediction units based on AAC information 629 , for example.

[0694] In some illustrative aspects, such as Figure 6 As shown, the PF NN 652 may include a plurality of layers including an input layer 651 , a plurality of hidden layers 653 , and an output layer 657 .

[0695] In some illustrative aspects, such as Figure 6 As shown, multiple layers of the PF NN 652 may be connected via multiple edges.

[0696] In some illustrative aspects, such as Figure 6 As shown, input layer 651 may be configured to receive NN input including AAC information 629 .

[0697] In some illustrative aspects, such as Figure 6 As shown, the output layer 657 may be configured to provide the PF setting 603 to the prediction unit, for example, based on the AAC information 629 .

[0698] In some illustrative aspects, such as Figure 6 As shown, the estimation function 637 can be applied to the hidden layer 653.

[0699] In some illustrative aspects, such as Figure 6 As shown, the estimation function 637 may include a non-linear function.

[0700] In other aspects, the estimation function 637 may include a linear function 637 .

[0701] Return to reference Figure 4 , a NN-based AAC system (eg, the NN-based AA system 400 ) may utilize a PF NN, which may be configured to process a NN input of extracted AAC information including one or more extracted AAC parameters.

[0702] In some demonstrative aspects, for example, extracted AAC information of one or more extracted AAC parameters may be utilized in addition to or in lieu of AAC information 429 .

[0703] In some demonstrative aspects, the extracted AAC information may correspond to one or more AAC parameters that may not be directly sensed and / or detected, such as via one or more information sources 420 .

[0704] In some demonstrative aspects, for example, based on some or all of the noise inputs in plurality of noise inputs 404, the extracted AAC information of the extracted AAC parameters may be extracted, for example, as described below.

[0705] In some demonstrative aspects, the extracted AAC information of the extracted AAC parameters may be used, for example, to model one or more parameters that may not be sensed and / or detected via, for example, one or more information sources 420 .

[0706] In some demonstrative aspects, the extracted AAC information of the extracted AAC parameters may include information extracted from some or all of AAC information 429 .

[0707] In some demonstrative aspects, the extracted AAC information of the extracted AAC parameters may be implemented, for example, to provide a technical solution that may be used to simplify the AAC architecture of the NN-based AAC system 400 .

[0708] In some demonstrative aspects, the extracted AAC information of the extracted AAC parameters may be implemented to, for example, provide a technical solution to reduce the computational workload of the NN-based AAC system 400 .

[0709] refer to Figure 7 , which schematically illustrates a NN-based AAC system 700 , according to some illustrative aspects.

[0710] For example, the AAC system 100 ( Figure 1 ) may include one or more elements of the NN-based AAC system 700 and / or may perform one or more operations and / or functions of the NN-based AAC system 700.

[0711] For example, the NN-based AAC system 700 may include the NN-based AAC system 400 ( Figure 4) and / or may implement the NN-based AAC system 400 ( Figure 4 )'s one or more operations and / or functions.

[0712] In some illustrative aspects, such as Figure 7 As shown, the AAC system 700 may include a PF NN 752 that may be trained to generate PF settings for a prediction unit 760, eg, as described above.

[0713] In some illustrative aspects, such as Figure 7 As shown, the prediction unit 760 can be configured to predict and / or estimate an estimation function or prediction function 763 to be applied to one or more noise inputs 704 to, for example, generate a sound control pattern 709, for example, as described above.

[0714] In some illustrative aspects, such as Figure 7 As shown, noise input 704 may be provided by one or more acoustic sensors and may represent acoustic noise from a noise source, for example, as described above.

[0715] For example, PF NN 452( Figure 4 ) may include one or more elements of PF NN 752 and / or may perform one or more operations and / or functions of PFNN 752; and / or prediction unit 460 ( Figure 4 ) may include one or more elements of the prediction unit 760 and / or may perform one or more operations and / or functions of the prediction unit 760.

[0716] In some demonstrative aspects, PF NN 752 may be trained to generate PF settings for prediction unit 760, for example, based on AAC information 729 from one or more AAC information sources. For example, AAC information 729 may include information from information source 120 ( Figure 1 ) of AAC information 129( Figure 1 ), for example, as described above.

[0717] In some demonstrative aspects, NN-based AAC system 700 may include an AAC parameter extractor 720 , which may be configured to provide extracted AAC information 725 corresponding to one or more AAC parameters, eg, as described below.

[0718] In some illustrative aspects, such as Figure 7 As shown, the AAC parameter extractor 720 may be configured to extract the extracted AAC information 725 , for example, based on acoustic noise information from the one or more noise inputs 704 .

[0719] In some demonstrative aspects, AAC parameter extractor 720 may be configured to generate extracted AAC information 725 , for example, based on AAC information 729 (eg, in addition to or instead of some or all of plurality of noise inputs 704 ).

[0720] In some demonstrative aspects, AAC parameter extractor 720 may be configured to generate extracted AAC information 725 , eg, by applying a predefined reference noise extraction to multiple noise inputs 704 and / or AAC information 729 , eg, as described below.

[0721] In some demonstrative aspects, AAC parameter extractor 720 may be configured to generate extracted AAC information 725 according to a predefined reference noise extraction function, which may be based on, for example, a principal component analysis (PCA) technique.

[0722] In other aspects, any other additional or alternative techniques, methods, and / or mechanisms may be implemented to generate the extracted AAC information 725 .

[0723] In some demonstrative aspects, AAC parameter extractor 720 may be configured to determine one or more principal components, for example, based on plurality of noise inputs 704 and / or AAC information 729 (eg, based on PCA techniques).

[0724] In some demonstrative aspects, AAC parameter extractor 720 may be configured to determine extracted AAC parameter information 725 , for example, based on sensor information 704 from one or more sensors, eg, as described below.

[0725] In some demonstrative aspects, AAC parameter extractor 720 may include a PCA extractor, which may be configured to determine extracted AAC parameter information 725 , eg, in the time domain or the frequency domain, based on PCA of sensor information 704 .

[0726] In some demonstrative aspects, AAC parameter extractor 720 may be configured to determine extracted AAC parameter information 725 , for example, based on a PCA of noise sensor information from one or more noise sensors.

[0727] In some demonstrative aspects, AAC parameter extractor 720 may be configured to determine extracted AAC parameter information 725, for example, based on a PCA of residual noise sensor information from one or more residual noise sensors.

[0728] In some demonstrative aspects, AAC parameter extractor 720 may be configured to determine extracted AAC parameter information 725 , for example, based on PCA of monitoring sensor information from one or more monitoring sensors.

[0729] In some demonstrative aspects, AAC parameter extractor 720 may be configured to determine extracted AAC parameter information 725 , for example, based on PCA of virtual sensor information from one or more virtual sensors.

[0730] In some illustrative aspects, the AAC parameter extractor 720 can be configured to determine extracted AAC parameter information 725, which parameter information includes, for example, at least one of the following: road type information, cabin state information, passenger position information, vehicle system state information, noise characteristic information and / or noise state information, for example, as described above.

[0731] In other aspects, the AAC parameter extractor 720 may be configured to determine extracted AAC parameter information 725 that includes any other additional or alternative types of information.

[0732] In some illustrative aspects, such as Figure 7 As shown, the PF NN 752 may be trained to generate PF settings for a prediction unit 760 based on a plurality of AAC parameters 725, for example.

[0733] In some illustrative aspects, such as Figure 7 As shown, the PF NN 752 may be trained to generate PF settings for a prediction unit 760 based on the AAC information 729 and the plurality of AAC parameters 725, for example.

[0734] In some illustrative aspects, such as Figure 7 As shown, the PF NN 752 may be configured to generate PF settings for the prediction unit 760, for example based on a NN input, which may include extracted AAC information 725, for example, in addition to or in lieu of one or more components of the AAC information 729.

[0735] In some demonstrative aspects, prediction unit 760 may determine prediction function 763 based on a PF setting of prediction unit 760 .

[0736] In some illustrative aspects, a controller (e.g., AAC controller 102 ( Figure 1 ) and / or controller 193( Figure 1 )) can be configured to determine the sound control mode 709, for example, by applying the prediction function 763 to the noise input 704, for example, as described above.

[0737] In some illustrative aspects, such as Figure 7 As shown, sound control mode 709 can be applied to one or more acoustic transducers, for example, to control sound within a sound control zone, for example, as described above.

[0738] In some demonstrative aspects, PF NN 752 may be trained and / or calibrated, for example, based on noise errors, e.g., as described below.

[0739] In some illustrative aspects, the noise error at the error sensing location can be based on, for example, a comparison of an acoustic signal at the error sensing location, denoted as Y, and an acoustic signal at the error sensing location, denoted as The differences between the acoustic sound control modes.

[0740] In some illustrative aspects, such as Figure 7 As shown, the acoustic sound control mode at the error sensing location The TTF (eg, STF) between the acoustic transducer and the error sensing location may be based on the sound control pattern 709 and the error sensing location, which may include, for example, a physical error sensing location or a virtual error sensing location, for example, as described above.

[0741] In some demonstrative aspects, PF NN 752 may be trained, for example, based on an error signal, which may correspond to a noise error at an error sensing location, eg, as described above.

[0742] In some illustrative aspects, for example, during training of PF NN 752, a predefined noise pattern may be generated, a predefined set of AAC information 729 and a predefined set of extracted AAC information 725 may be provided as NN inputs to PF NN 752, and an error signal may be determined.

[0743] In some illustrative aspects, the error signal may be repeatedly determined, for example, during training of PF NN 752 , eg, for a plurality of different predefined noise patterns, a plurality of different predefined settings of AAC information 729 , and / or a plurality of different predefined settings of extracted AAC information 725 .

[0744] In some illustrative aspects, for example, during training of PF NN 752, PF NN 752 may be trained, for example, based on a training criterion of minimizing or eliminating an error signal.

[0745] refer to Figure 8 , which schematically illustrates a NN-based PF including a PF NN 852 for configuring settings for a prediction unit 860, according to some demonstrative aspects.

[0746] For example, PF NN 752( Figure 7 ) may include one or more elements of PF NN 852 and / or may perform one or more operations and / or functions of PFNN 852; and / or prediction unit 760 ( Figure 7) may include one or more elements of the prediction unit 860 and / or may perform one or more operations and / or functions of the prediction unit 860.

[0747] In some demonstrative aspects, PF NN 852 may be trained to generate NN output 813 that includes PF settings for prediction unit 860 .

[0748] In some illustrative aspects, such as Figure 8 As shown, the prediction unit 860 may be configured to predict and / or estimate a prediction function 803 that will be applied to the noise input 804 to generate a sound control pattern 809 , for example.

[0749] In some illustrative aspects, such as Figure 8 As shown, the PF NN 852 can be configured to generate PF settings for the prediction unit 860, for example, based on the NN input 801, for example, as described below. For example, the NN output 813 can include a plurality of PF coefficients to configure the prediction function 803 of the prediction unit 860, for example, as described above.

[0750] In some illustrative aspects, NN input 801 can be configured to receive AAC information 829, for example, from one or more information sources. For example, AAC information 829 can include information from information source 120 ( Figure 1 ) of AAC information 129( Figure 1 ).

[0751] In some demonstrative aspects, AAC information 829 may include one or more vehicle / cabin parameters corresponding to settings in a vehicle cabin, for example, in a vehicle AAC implementation, eg, as described above.

[0752] In other aspects, the AAC information 829 may include any other additional or alternative information, eg, as described above.

[0753] In some illustrative aspects, such as Figure 8 As shown, the PF NN 852 can be configured to generate PF settings for the prediction unit 860, for example, based on extracted feature information, which can be extracted, for example, based on sensor information from one or more sensors (e.g., one or more physical sensors and / or virtual sensors, such as acoustic sensors, monitoring sensors, and / or residual noise sensors), for example, as described below.

[0754] In some demonstrative aspects, NN input 801 may be configured to receive extracted AAC information 825 from AAC parameter extractor 820 , for example, as described below.

[0755] In some demonstrative aspects, AAC parameter extractor 820 may be configured to extract the extracted AAC information 825, for example, from AAC information 829 and / or from noise input 804. For example, AAC parameter extractor 820 may include AAC parameter extractor 720 ( Figure 7 ) and / or may execute one or more elements of the AAC parameter extractor 720 ( Figure 7 )'s one or more operations and / or functions.

[0756] In some illustrative aspects, such as Figure 8 As shown, the AAC parameter extractor 820 may be configured to extract the extracted AAC information 825, for example, based on PCA information 836, which may be determined based on the noise input 804, for example, as described below.

[0757] In some illustrative aspects, such as Figure 8 As shown, the noise input 804 may be transformed from the time domain to the frequency domain using, for example, a fast Fourier transform (FFT) 832 .

[0758] In some illustrative aspects, such as Figure 8 As shown, PCA information 836 may include one or more principal components, which may be determined, for example, by applying PCA to the noise input 804, for example, in the frequency domain. In other aspects, PCA information 836 may include one or more principal components, which may be determined, for example, by applying PCA to the noise input 804, for example, in the time domain.

[0759] In some illustrative aspects, such as Figure 8 As shown, the AAC parameter extractor 820 may be configured to determine extracted AAC information 825 based on one or more principal components 836 , for example.

[0760] In some demonstrative aspects, PF NN 852 may be trained to generate NN output 803 including PF settings for prediction unit 860 , for example, based on AAC information 829 and extracted AAC information 825 .

[0761] In some illustrative aspects, such as Figure 8 As shown, the PF NN 852 may include a plurality of layers including an input layer 851 , a plurality of hidden layers 853 , and an output layer 857 .

[0762] In some illustrative aspects, such as Figure 8 As shown, multiple layers of the PF NN 852 may be connected via multiple edges / nodes.

[0763] In some illustrative aspects, such as Figure 8As shown, input layer 851 may be configured to receive AAC information 829 and extracted AAC information 825 .

[0764] In some illustrative aspects, such as Figure 8 As shown, the output layer 857 may be configured to provide a PF setting to a prediction unit 860 , for example, based on the AAC information 829 and the extracted AAC information 825 .

[0765] In some demonstrative aspects, AAC parameter extractor 820 may be configured to generate extracted AAC information 825 including, for example, extracted road type information based on noise input 804 and / or AAC information 829 , for example, as described below.

[0766] In some demonstrative aspects, AAC parameter extractor 820 may be configured to generate extracted AAC information 825 including any other additional or alternative extracted AAC information, eg, as described above.

[0767] In some demonstrative aspects, AAC parameter extractor 820 may be configured to determine road type information, for example, based on road noise patterns corresponding to a plurality of road types and / or a plurality of reference noise patterns, eg, as described below.

[0768] refer to Fig. 9 , which schematically illustrates an AAC parameter extraction scheme 900 for configuring extraction of AAC information, according to some demonstrative aspects.

[0769] In some illustrative aspects, AAC parameter extraction scheme 900 may be configured to extract road type parameters, e.g., as described below. In other aspects, AAC parameter extraction scheme 900 may be configured to extract any other additional or alternative AAC parameter information, e.g., as described above.

[0770] In some illustrative aspects, such as Fig. 9 As shown, the AAC parameter extraction scheme 900 may include an AAC parameter extractor 920. For example, the AAC parameter extractor 820 ( Figure 8 ) may include one or more elements of the AAC parameter extractor 920 and / or may perform one or more operations and / or functions of the AAC parameter extractor 920.

[0771] In some illustrative aspects, AAC parameter extractor 920 may be configured to extract AAC parameter information including, for example, road type parameters based on noise input 904, which may be obtained from, for example, a reference acoustic sensor and / or a monitoring sensor, such as reference noise sensor 119 ( Figure 1 ) and / or the monitoring sensor at the monitoring sensing location 103 ( Figure 2) receives the noise input.

[0772] In some illustrative aspects, such as Fig. 9 As shown, the AAC parameter extractor 920 may extract AAC parameter information including, for example, road type parameters based on a 2D principal component reference map 930 .

[0773] In some demonstrative aspects, 2D principal component reference map 930 may include a plurality of 2D principal components 932 corresponding to a respective plurality of AAC parameter options (eg, a plurality of road types).

[0774] In some illustrative aspects, for example, in an AAC system, such as AAC system 700 ( Figure 7 ) during training to determine the 2D principal component reference map 930.

[0775] In some illustrative aspects, 2D principal component reference map 930 may be determined, for example, by applying predefined noise patterns corresponding to multiple AAC parameter options (e.g., multiple road types) (e.g., from a database (DB) 940 of predefined AAC parameter noise patterns (e.g., road noise patterns corresponding to multiple road types)) to reference noise sensor 919. For example, a specific 2D principal component 932 corresponding to a specific road type may be determined, for example, by applying a predefined noise pattern corresponding to a corresponding road type from DB 940 to reference noise sensor 919.

[0776] In some illustrative aspects, such as Fig. 9 As shown, the AAC parameter extraction scheme 900 may include an FFT block 931 configured to convert the noise input 904 from the reference noise sensor 919 from the time domain to the frequency domain.

[0777] In some illustrative aspects, such as Fig. 9 As shown, the AAC parameter extractor 920 may extract one or more principal components 936 from the noise input 904 in the frequency domain, for example based on PCA of the noise input 904 in the frequency domain.

[0778] In some illustrative aspects, such as Fig. 9 As shown, the AAC parameter extractor 920 determines AAC parameter information including, for example, road type parameters based on correlation between one or more principal components 936 from the noise input 904 and a plurality of 2D principal components 936 in the 2D principal component reference map 930 .

[0779] Return to reference Figure 4In some illustrative aspects, the prediction filter NN 452 can be trained to generate predictive filter settings for the prediction filter 460, for example, based on a path transfer function (e.g., TTF or STF) between the acoustic transducer 408 and one or more acoustic sensing locations (e.g., the error sensing location 407).

[0780] In some illustrative aspects, predictive filter NN 452 may be trained to generate predictive filter settings for predictive filter 460 based on a path transfer function between acoustic transducer 408 and error sensing location 407 , eg, as described below.

[0781] In other aspects, the predictive filter NN 452 can be trained to generate predictive filter settings for the predictive filter 460 based on one or more path transfer functions between the acoustic transducer 408 and one or more additional or alternative acoustic sensing locations (e.g., one or more noise sensing locations, one or more residual noise sensing locations, and / or one or more monitoring sensing locations), e.g., as described above.

[0782] In some illustrative aspects, prediction filter NN 452 can be trained to generate prediction filter settings for prediction filter 460, for example, based on TTF (e.g., STF) settings between acoustic transducer 408 and one or more acoustic sensing locations (e.g., error sensing locations 407), e.g., as described below.

[0783] In some illustrative aspects, a TTF (STF) NN, such as TTF (STF) NN 154 ( Figure 1 ) to determine a TTF (STF) setting for the TTF (STF) between the acoustic transducer 408 and the acoustic sensing location, for example, as described below.

[0784] In some illustrative aspects, a TTF (STF) NN, such as TTF (STF) NN 154 ( Figure 1 ) to determine a TTF (STF) setting of the TTF (STF) between the acoustic transducer 408 and the error sensing location 407, for example, as described below.

[0785] In some illustrative aspects, a TTF(STF)NN, for example, TTF(STF)NN 154 ( Figure 1 ) can be trained to, for example, generate a TTF (STF) setting for TTF (STF) based on AAC information 429, for example, as described below.

[0786] refer to Fig.10 , which schematically illustrates an NN-based AAC system 1000 according to some illustrative aspects. For example, the AAC system 100 ( Figure 1 ) may include one or more elements of AAC system 1000 and / or may perform one or more operations and / or functions of AAC system 1000.

[0787] In some illustrative aspects, such as Fig.10 As shown, the NN-based AAC system 1000 may include a PF NN 1052, which may be trained to generate PF settings for a prediction unit 1060, e.g., as described above.

[0788] In some illustrative aspects, such as Fig.10 As shown, the prediction unit 1060 can be configured to predict and / or estimate an estimation function or prediction function 1063 to be applied to one or more noise inputs 1004 to, for example, generate a sound control pattern 1009, for example, as described above.

[0789] In some illustrative aspects, sound control mode 1009 may be applied to control acoustic transducer 1008 , such as to control sound at a sound control zone, eg, as described above.

[0790] In some illustrative aspects, such as Fig.10 As shown, noise input 1004 may be provided by one or more acoustic sensors and may represent acoustic noise from a noise source, for example, as described above.

[0791] For example, PF NN 452( Figure 4 ) may include one or more elements of PF NN 1052 and / or may perform one or more operations and / or functions of PFNN 1052; and / or prediction unit 460 ( Figure 4 ) may include one or more elements of the prediction unit 1060 and / or may perform one or more operations and / or functions of the prediction unit 1060.

[0792] In some illustrative aspects, such as Fig.10 As shown, the PF NN 1052 may be trained to generate PF settings for the prediction unit 1060, for example, based on AAC information from one or more AAC information sources and / or based on AAC information extracted from an AAC parameter extractor, for example, as described above.

[0793] In some illustrative aspects, PF NN 1052 may be trained to generate PF settings for prediction unit 1060 based on TTF (STF) 1031 between acoustic transducer 1008 and one or more acoustic sensing locations, for example, as described below.

[0794] In some illustrative aspects, PF NN 1052 may be trained to generate predictive filter settings for predictive filter 1060 based on a path transfer function between acoustic transducer 1008 and error sensing location 1007 , eg, as described below.

[0795] In other aspects, the PF NN 1052 can be trained to generate predictive filter settings for the predictive filter 1060 based on one or more path transfer functions between the acoustic transducer 1008 and one or more additional or alternative acoustic sensing locations (e.g., one or more noise sensing locations, one or more residual noise sensing locations, and / or one or more monitoring sensing locations).

[0796] In one example, the PF NN 1052 can be trained to detect the presence of an acoustic transducer 1008 and a monitoring sensing location 103 ( Figure 2 ) to generate a prediction filter setting for the prediction filter 1060, which path transfer function can be used to estimate a virtual error at the error sensing location 1007, for example, as described above.

[0797] In some illustrative aspects, such as Fig.10 As shown, the PF NN 1052 may be trained to generate PF settings for the prediction unit 1060 based on, for example, the TTF (STF) 1031 between the acoustic transducer 1008 and the error sensing location 1007 (eg, within the sound control zone).

[0798] In some illustrative aspects, such as Fig.10 As shown, the PF NN 1052 can be trained to, for example, detect the difference between one or more transducers 1008 and one or more acoustic sensing locations (e.g., error sensing location 1007), monitoring locations ( Figure 2 ) and / or noise sensing location 105 ( Figure 2 ) to learn the TTF NN space, for example, as described below.

[0799] In some illustrative aspects, error sensing location 1007 may include a sound control zone, such as sound control zone 110 ( Figure 2 ) in the error sensing location. For example, the noise error at the error sensing location 1007 can be detected by one or more residual noise sensors, such as the residual noise sensor 121 ( Figure 1 ) sensing. For example, a monitoring microphone, such as the error microphone 121 ( Figure 1 ) to sense, for example, noise errors that may be caused by acoustic changes of the NN-based AAC system 1000 (e.g., acoustic changes in a vehicle cabin implementing the NN-based AAC system 1000).

[0800] In some illustrative aspects, such as Fig.10 As shown, the AAC system 1000 may include a TTF (STF) NN 1054 that may be trained to generate TTF (STF) setting information 1096 that represents an estimated TTF (STF) setting between an acoustic transducer 1008 and one or more acoustic sensing locations (e.g., including an error sensing location 1007), e.g., as described below. For example, the TTF (STF) NN 154 ( Figure 1 ) may include one or more elements of TTF(STF)NN 1054 and / or may perform one or more operations and / or functions of TTF(STF)NN 1054.

[0801] In some demonstrative aspects, TTF (STF) NN 1154 and / or PF NN 1052 can be trained separately. In other aspects, TTF (STF) NN 1154 and / or PF NN 1052 can be trained in parallel.

[0802] In some demonstrative aspects, TTF (STF) NN 1054 may be trained and / or calibrated, for example, based on noise errors at one or more acoustic sensing locations (eg, including error sensing location 1007 ), eg, as described below.

[0803] In some illustrative aspects, an AAC system (e.g., AAC system 1000) may utilize a TTF(STF)NN (e.g., TTF(STF)NN 1054) to estimate the TTF(STF) between the acoustic transducer 1008 and one or more acoustic sensing locations (e.g., including the error sensing location 1007) to provide a more accurate estimate of the actual TTF(STF).

[0804] In some illustrative aspects, the noise error at error sensing location 1007 may be based on, for example, a difference between an acoustic signal at error sensing location 1007 and an acoustic sound control pattern at error sensing location 1007 .

[0805] In some illustrative aspects, the acoustic sound control pattern at error sensing location 1007 may be based on sound control pattern 1009 and TTF (STF) 1031 between acoustic transducer 1008 and error sensing location 1007 .

[0806] In some demonstrative aspects, the acoustic signal at error sensing location 1007 may be based on an impulse response of an acoustic channel between the noise source and error sensing location 1007 .

[0807] In some illustrative aspects, the TTF (STF) NN 1054 can be trained to generate TTF (STF) setting information 1096, e.g., for a plurality of acoustic transducer sensor pairs in the AAC system 1000. For example, the acoustic transducer sensor pairs (e.g., each acoustic transducer sensor pair) can include different combinations of acoustic transducers and acoustic sensors (e.g., error microphones, monitoring microphones, virtual sensors, and / or noise microphones) of the AAC system 1000.

[0808] In some illustrative aspects, TTF (STF) settings for each scenario and each acoustic transducer-sensor pair can be implemented to provide a technical solution for supporting improved (eg, optimal) settings of TTF (STF).

[0809] In some demonstrative aspects, TTF (STF) NN 1054 may be trained, for example, based on error signal 1057 , which may correspond to a noise error at error sensing location 1007 .

[0810] In some demonstrative aspects, residual noise sensor 1021 (eg, an error microphone) may be configured to generate error signal 1057 , for example, during training of TTF (STF) NN 1054 .

[0811] In some illustrative aspects, for example, during training of the TTF (STF) NN 1054, a predefined noise pattern may be generated, a predefined set of AAC information may be provided as a NN input to the TTF (STF) NN 1054, and an error signal 1057 may be determined, for example, as described below.

[0812] In some illustrative aspects, for example, during training of the TTF (STF) NN 1054, determination of the error signal 1057 may be repeated, e.g., as described below, for a plurality of different predefined noise patterns and / or a plurality of different predefined settings of AAC information.

[0813] In some illustrative aspects, for example, during training of the TTF (STF) NN 1054, the TTF (STF) NN 1054 may be trained, for example, based on a training criterion of minimizing or eliminating the error signal 1057, for example, as described below.

[0814] refer to Fig.11 , which schematically illustrates a training scheme 1100 for training a TTF (STF) NN 1154 according to some illustrative aspects.

[0815] For example, TTF(STF)TF NN 1054( Fig.10) may include one or more elements of TTF(STF)NN 1154 and / or may perform one or more operations and / or functions of TTF(STF)NN 1154.

[0816] In some illustrative aspects, TTF (STF) NN 1154 may be trained to generate TTF (STF) settings 1155 of TTF (STF) between acoustic transducer 1108 and acoustic sensing locations 1107 (eg, error sensing locations, monitoring sensing locations, and / or noise sensing locations).

[0817] In some demonstrative aspects, TTF (STF) NN 1154 may be trained to generate TTF (STF) settings 1155, for example, based on AAC information 1129. For example, AAC information 1129 may include information from information source 120 ( Figure 1 ) of AAC information 129( Figure 1 ).

[0818] In some illustrative aspects, such as Fig.11 As shown, the TTF (STF) NN 1154 can be trained based on an acoustic signal 1157 sensed at the acoustic sensing location 1107. In one example, the signal 1157 can include an error signal from an error microphone 1121 at the error sensing location 1107.

[0819] In some demonstrative aspects, error signal 1157 may be based on a noise difference between first signal 1132 and second signal 1134 , eg, as described below.

[0820] In some illustrative aspects, the first signal 1132 can be based on a sound control pattern 1123 from a noise generator 1120 that can be applied to the acoustic transducer 1108 and received by an error microphone 1121 located at an acoustic sensing location 1107 (e.g., an error sensing location 1107), for example via an actual TTF (STF) between the acoustic transducer 1108 and the acoustic sensing location 1107 (e.g., the error sensing location 1107).

[0821] In some demonstrative aspects, second signal 1134 may be generated, for example, by applying TTF (STF) settings 1155 generated by TTF (STF) NN 1154 to sound control pattern 1123 from noise generator 1120 .

[0822] In some illustrative aspects, for example, during training of the TTF (STF) NN 1154, the predefined noise pattern 1123 may be generated by the noise generator 1120, the predefined settings of the AAC information 1129 may be provided as NN inputs to the TTF (STF) NN 1154, and the error signal 1157 may be determined.

[0823] In some illustrative aspects, for example, during training of the TTF (STF) NN 1154, the determination of the sensed acoustic signal 1157 (e.g., error signal 1157) may be repeated, for example, for a plurality of different predefined noise patterns 1123 and / or a plurality of different predefined settings of AAC information 1129.

[0824] In some illustrative aspects, for example, during training of the TTF (STF) NN 1154 , the TTF (STF) NN 10154 may be trained, for example, based on a training criterion of minimizing or eliminating a sensed acoustic signal 1157 , such as an error signal 1157 .

[0825] In some demonstrative aspects, the output of TTF(STF)NN 1154 can include a vector of auxiliary transfer functions. For example, during training, this vector of auxiliary transfer functions can be compared to an estimated TTF(STF), for example, based on signal 1132.

[0826] refer to Fig.12 , which schematically illustrates a NN-based AAC system 1200 according to some illustrative aspects.

[0827] For example, the AAC system 100 ( Figure 1 ) may include one or more elements of AAC system 1200 and / or may perform one or more operations and / or functions of AAC system 1200.

[0828] In some demonstrative aspects, AAC system 1200 may include a multiple-input multiple-output (MIMO) AAC system that may be configured to mitigate noise from multiple noise sources 1220 , eg, as described below.

[0829] In some illustrative aspects, such as Fig.12 As shown, the NN-based AAC system 1200 may include a PF NN 1252 that may be trained to generate PF settings for a prediction unit 1260 .

[0830] In some illustrative aspects, such as Fig.12As shown, the prediction unit 1260 may be configured to predict and / or estimate an estimation function or prediction function 1263 to be applied to one or more noise inputs 1204 to generate a sound control pattern 1209 , for example, during an operating mode of the AAC system 1200 .

[0831] In some demonstrative aspects, sound control mode 1209 may be applied to control one or more acoustic transducers 1208 , such as to control sound at a sound control zone, eg, as described above.

[0832] In some illustrative aspects, such as Fig.12 As shown, noise input 1204 may be provided by one or more acoustic sensors and may represent acoustic noise from multiple noise sources 1220, for example, as described above.

[0833] For example, PF NN 452( Figure 4 ) may include one or more elements of PF NN 1052 and / or may perform one or more operations and / or functions of PFNN 1252; and / or prediction unit 460 ( Figure 4 ) may include one or more elements of the prediction unit 1260 and / or may perform one or more operations and / or functions of the prediction unit 1260.

[0834] In some demonstrative aspects, PF NN 1252 may be trained to generate PF settings for prediction unit 1260, eg, based on AAC information from one or more AAC information sources and / or based on AAC information extracted from an AAC parameter extractor, eg, as described above.

[0835] For example, Fig.12 As shown, the PF NN 1252 may be trained, for example, using the TTF (STF) in a training loop, for example, according to a forward propagation during a training session.

[0836] refer to Fig.13 , which schematically illustrates a graph 1320 depicting performance of active noise reduction (ANR) of a NN-based AAC system versus time, according to some illustrative aspects.

[0837] In one example, the AAC system 100 ( Figure 1 )、AAC System 400( Figure 4 )、AAC System 800( Figure 8 ) and / or AAC system 1200 ( Fig.12 ) can be configured to provide a technical solution for supporting ANR performance that is generally similar to or even better than the ANR performance of Figure 1320.

[0838] In some illustrative aspects, as can be seen from graph 1320, the NN-based AAC system can achieve effective ANR after a relatively short period of time (e.g., less than 1 second).

[0839] refer to Fig.14A , which schematically illustrates a NN-based controller 1400 according to some illustrative aspects. In some aspects, the AAC controller 202 ( Figure 1 ) and / or controller 193( Figure 1 ) can perform, for example, one or more functions and / or operations of controller 1400.

[0840] In some illustrative aspects, controller 1400 may receive AAC information 1429, for example, including AAC information 129 ( Figure 1 ).

[0841] In some illustrative aspects, controller 1400 may receive multiple inputs 1404 (e.g., including input 104 ( Figure 1 )), which represents a plurality of predetermined noise sensing locations (eg, location 105 ( Figure 2 )) The controller 1400 may generate a sound control signal 1412 to control at least one acoustic transducer 1414, such as the acoustic transducer 108 ( Figure 1 ).

[0842] In some demonstrative aspects, controller 1400 may include a NN-based estimator (“prediction unit”) 1410 for estimating a sound control signal 1412 , for example, based on NN input 1408 (which may be based on at least input 1404 ).

[0843] In some illustrative aspects, NN-based estimator 1410 may include NN 150 ( Figure 1 ) and / or may execute one or more elements of NN 150( Figure 1 )'s one or more operations and / or functions.

[0844] In some demonstrative aspects, NN-based estimator 1410 may include PF NN 152 ( Figure 1 ) and / or may execute one or more elements of PF NN 152 ( Figure 1 )'s one or more operations and / or functions.

[0845] In some demonstrative aspects, NN-based estimator 1410 may include PF NN 452 ( Figure 1 ) and / or PF 460( Figure 4 ) and / or may execute one or more elements of PF NN 452 ( Figure 1) and / or PF 460( Figure 4 )'s one or more operations and / or functions.

[0846] In some demonstrative aspects, NN-based estimator 1410 may include TTF (STF) NN 154 ( Figure 1 ) and / or may perform TTF (STF) NN 154 ( Figure 1 )'s one or more operations and / or functions.

[0847] In some demonstrative aspects, NN-based estimator 1410 may include TTF (STF) NN 1054 ( Fig.10 ) and / or may perform TTF (STF) NN 1054 ( Fig.10 )'s one or more operations and / or functions.

[0848] In some demonstrative aspects, NN-based estimator 1410 may be trained to generate signal 1412 , for example, based on AAC information 1429 , eg, as described above.

[0849] In some illustrative aspects, NN-based estimator 1410 may include a NN, such as NN 150 ( Figure 1 ), the NN can be trained to directly generate signal 1412 based on NN input 1408 and AAC information 1429, for example, as described above.

[0850] In some demonstrative aspects, NN-based estimator 1410 may include PF NN (e.g., PF NN 452 ( Figure 4 )) and PF (e.g., PF 460 ( Figure 4 )). For example, PF NN (for example, PF NN 452 ( Figure 4 )) can be trained to generate PF (e.g., PF 460 ( Figure 4 )) of the PF setting, and PF (for example, PF 460 ( Figure 4 ))Signal 1412 can be generated by applying the PF setting to NN input 1408, for example, as described above.

[0851] In some illustrative aspects, for example, Fig.14A As shown, controller 1400 can include an extractor 1406 to extract a plurality of reference acoustic modes, such as disjoint reference acoustic modes, from input 1404. According to aspects, input 1408 can include one or more reference acoustic modes from the plurality of reference acoustic modes.

[0852] In some demonstrative aspects, controller 1400 may generate signal 1412 configured to reduce and / or eliminate noise generated by one or more noise sources, eg, as described above.

[0853] In some illustrative aspects, controller 1400 may generate a sound control signal 1412 that is configured to reduce and / or eliminate noise energy and / or amplitude of one or more acoustic modes within a sound control zone, while noise energy and / or amplitude of one or more other acoustic modes may not be affected within the sound control zone.

[0854] In some illustrative aspects, sound control signal 1412 may be configured to reduce and / or eliminate noise generated by one or more vehicle systems, eg, as described above.

[0855] In some illustrative aspects, the feature extractor 1406 can be configured to, for example, determine, update and / or adjust the setting of at least one acoustic pattern extractor parameter in real time based on the AAC information 1429, and, for example, determine multiple reference acoustic patterns for the NN input 1408 based on the acoustic pattern extractor parameter settings, e.g., as described above.

[0856] In other aspects, controller 1400 may not include extractor 1406. Thus, NN input 1408 may include input 1404 and / or any other input based on input 1404.

[0857] In some illustrative aspects, NN-based estimator 1410 may apply any suitable linear and / or nonlinear estimation function to input 1408. For example, the estimation function implemented by NN-based estimator 1410 may include a nonlinear estimation function, such as a radial basis function. In other aspects, any other additional or alternative suitable estimation functions may be implemented by NN-based estimator 1410.

[0858] In some demonstrative aspects, NN-based estimator 1410 may be configured to generate acoustic control signal 1412 , for example, based on one or more inputs of acoustic sensor information 1416 corresponding to one or more acoustic sensing locations, eg, as described above.

[0859] In some illustrative aspects, one or more inputs of acoustic sensor information 1416 may include information corresponding to one or more residual noise sensing locations, such as, for example, one or more residual noise sensing locations 107 ( Figure 2 )'s residual noise sensor information, for example, as described above.

[0860] In some illustrative aspects, one or more inputs of acoustic sensor information 1416 may include corresponding to one or more remaining monitoring locations, for example, one or more monitoring locations 102 ( Figure 2 )'s monitoring sensor information, for example, as described above.

[0861] In some demonstrative aspects, one or more inputs of acoustic sensor information 1416 may include information from an acoustic sensor at an acoustic sensing location, eg, as described above.

[0862] In some demonstrative aspects, one or more inputs of acoustic sensor information 1416 may include virtual sensor information corresponding to a virtual acoustic sensor at a virtual acoustic sensing location, eg, as described above.

[0863] In some illustrative aspects, one or more inputs of acoustic sensor information 1416 may include information corresponding to a location 107 ( Figure 2 ) at least one virtual microphone input of residual noise sensed by at least one virtual error sensor at at least one specific residual noise sensor location (“noise error”). For example, controller 1400 can evaluate the noise error at a specific residual noise sensor location based on input 1408 and predicted noise signal 1412, e.g., as described below.

[0864] In some demonstrative aspects, the NN-based estimator 1410 may include a multiple-input multiple-output (MIMO) prediction unit configured to, for example, generate a plurality of sound control patterns corresponding to the nth sample (e.g., including patterns represented as y1(n) . . . y M (n) to drive a plurality of M corresponding acoustic transducers 1414 based on input 1408, for example.

[0865] In some illustrative aspects, such as Fig.14A As shown, the controller 1400 may include an AFB mitigator component (“echo canceller”) 1418 configured to reduce, remove, and / or partially or completely cancel a portion of a signal generated by the acoustic transducer 1414 from the input signal 1404 .

[0866] In one example, the AFB mitigator 1418 can be configured to mitigate the acoustic transducer 1414, for example, one or more acoustic transducers 108 ( Figure 1 ) and one or more noise inputs 1402, for example, noise input 160 ( Figure 1 ) between the AFB.

[0867] In some illustrative aspects, AFB mitigator 1418 may be configured to generate AFB mitigation signal 1459 , for example, by applying AFB setting 1451 to acoustic transducer signal 1457 , which may include or may be based on acoustic transducer signal 1412 to be provided to acoustic transducer 1414 , e.g., as described below.

[0868] In some illustrative aspects, AFB settings 1451 may include a plurality of AFB mitigation coefficients to be applied to acoustic transducer signal 1457, e.g., to generate AFB mitigation signal 1459. For example, AFB mitigator 1418 may be configured to filter acoustic transducer signal 1457 according to AFB settings 1451, e.g., based on a product of acoustic transducer signal 1457 and the AFB mitigation coefficients.

[0869] In some demonstrative aspects, controller 1400 may be configured to determine the AFB mitigation signal, for example, by subtracting AFB mitigation signal 1459 from input signal 1404 .

[0870] In some demonstrative aspects, AFB mitigator 1418 may include AFB mitigation NN 1455, which may be trained to generate AFB settings 1451, for example, based on AAC information 1429. For example, AFB mitigation NN 1455 ( Figure 1 ) may include the AFB mitigation NN 1455 and / or may perform one or more operations and / or functions of the AFB mitigation NN 1455.

[0871] refer to Fig. 14B , which schematically illustrates a training scheme 1470 for training an AFB mitigation NN 1471 according to some illustrative aspects.

[0872] For example, AFB mitigates NN 1455 ( Fig.14A ) may include one or more elements of the AFB mitigation NN 1471 and / or may perform one or more operations and / or functions of the AFB mitigation NN 1471.

[0873] In some illustrative aspects, AFB mitigation NN 1471 may be trained to generate AFB mitigation settings 1473 to be applied for AFB mitigation between acoustic transducers 1478 and acoustic sensing locations 1477 (eg, error sensing locations, monitoring sensing locations, and / or noise sensing locations).

[0874] In some illustrative aspects, AFB mitigation NN 1471 may be trained to generate AFB settings 1473, for example, based on AAC information 1499. For example, AAC information 1499 may include AFB settings 1473 from information source 120 ( Figure 1) of AAC information 129( Figure 1 ).

[0875] In some illustrative aspects, such as Fig. 14B As shown, the AFB mitigation NN 1471 may be trained based on an acoustic signal 1475 sensed at an acoustic sensing location 1477. In one example, the signal 1475 may be sensed by an acoustic sensor 1491 located at the acoustic sensing location 1477.

[0876] In one example, the acoustic sensor 1491 may include an acoustic sensor such as the reference noise sensor 119 ( Figure 1 ).

[0877] In another example, the acoustic sensor 1491 may include a residual noise sensor, such as the residual noise sensor 121 ( Figure 1 ).

[0878] In some demonstrative aspects, signal 1475 may be based on a difference between first signal 1481 and second signal 1483 , eg, as described below.

[0879] In some illustrative aspects, the first signal 1481 may be based on a sound control pattern 1485 from a noise generator 1489, which may be applied to the acoustic transducer 1478 and received by the acoustic sensor 1491 at the acoustic sensing location 1477, e.g., via an actual TTF (STF) between the acoustic transducer 1478 and the acoustic sensing location 1477.

[0880] In some demonstrative aspects, second signal 1483 may be generated, for example, by applying AFB settings 1473 generated by AFB mitigation NN 1471 to sound control mode 1485 from noise generator 1489 .

[0881] In some illustrative aspects, for example, during training of AFB mitigation NN 1471 , predefined noise pattern 1485 may be generated by noise generator 1489 , predefined settings of AAC information 1499 may be provided as NN input to AFB mitigation NN 1471 , and signal 1475 may be determined.

[0882] In some illustrative aspects, for example, during training of AFB mitigation NN 1471 , determination of sensed acoustic signal 1475 may be repeated, for example, for multiple different predefined noise patterns 1485 and / or multiple different predefined settings of AAC information 1499 .

[0883] In some illustrative aspects, for example, during training of AFB mitigation NN 1471 , AFB mitigation NN 1471 may be trained to minimize or eliminate sensed acoustic signals 1475 , for example, based on training criteria.

[0884] Reference now Fig.15 , which schematically illustrates a MIMO prediction unit 1500 according to some illustrative aspects. In some illustrative aspects, the NN-based estimator 1410 ( Fig.14A ) may include the MIMO prediction unit 1500, and / or perform one or more functions and / or operations of the MIMO prediction unit 1500.

[0885] In some illustrative aspects, such as Fig.15 As shown, the prediction unit 1500 may be configured to receive, for example, AAC information 129 ( Figure 1 )'s AAC information 1529.

[0886] In some illustrative aspects, such as Fig.15 As shown, the prediction unit 1500 can be configured to receive an input 1512, which can be based on the noise input 1402 ( Fig.14A ). For example, input 1512 can be based on extractor 1406 ( Fig.14A ), for example, as described above.

[0887] In some illustrative aspects, such as Fig.15 As shown, the prediction unit 1500 can be configured to drive a speaker array 1502, which includes M acoustic transducers, for example, the acoustic transducer 108 ( Figure 2 ). For example, the prediction unit 1500 may be based on the input 1408 ( Fig.14A ) to generate M sound control modes y1(n)…y M (n) to drive a plurality of M corresponding acoustic transducers (e.g., acoustic transducer 108 ( Figure 2 )).

[0888] In some illustrative aspects, interference (crosstalk) may occur between two or more (e.g., some or all) of the M acoustic transducers of array 1502, for example, when two or more (e.g., some or all) of the M acoustic transducers generate a control noise pattern, for example, simultaneously.

[0889] In some illustrative aspects, prediction unit 1500 may generate output 1501 configured to control array 1502 to generate a substantially optimal sound control pattern, e.g., while simultaneously optimizing an input signal to each speaker in array 1502. For example, prediction unit 1500 may control multi-channel speakers of array 1502, e.g., while simultaneously eliminating interference between speakers.

[0890] refer to Fig.16 , which schematically illustrates an implementation of a controller 1600 in an AAC system according to some illustrative aspects. For example, the controller 193 ( Figure 1 )、Controller 1400( Fig.14A ) and / or NN-based AAC system 400 ( Figure 4 ) may include a controller 1600 ( Fig.16 ) and / or can perform one or more operations and / or functions of controller 1600.

[0891] In some illustrative aspects, controller 1600 may be configured to receive input 1612 including residual noise from multiple microphones (RMIC) and generate output signal 1601 to drive speaker array 1602 including M acoustic transducers (e.g., three speakers or any other number of speakers). For example, input 1612 may include input 106 ( Figure 1 ), input 1416( Fig.14A ) and / or enter 404 ( Figure 4 ).

[0892] In some illustrative aspects, such as Fig.16 As shown, the controller 1600 may include a plurality of prediction filters (denoted as PF) on the path between the input 1612 and the output signal 1601 .

[0893] In some illustrative aspects, controller 1600 may include a PF NN, such as PF NN 152 ( Figure 1 ) and / or PF NN452( Figure 4 ), the PF NN may be configured, for example, based on the AAC information 129 ( Figure 1 ) to configure, determine, update and / or set one or more parameters of one or more PFs in one or more paths between input 1612 and output signal 1601, for example, as described above.

[0894] In some illustrative aspects, controller 1600 may include an AFB NN, such as AFB NN 155 ( Figure 1 ) and / or AFBNN 1455( Fig.14A), the AFB NN may be configured, for example, based on the AAC information 129 ( Figure 1 ) to configure, determine, update and / or set one or more parameters of one or more AFB settings (e.g., EC settings) to be applied on one or more paths between the output signal 1601 and the input 1612, for example, as described above.

[0895] Return to reference Figure 1 In some demonstrative aspects, AAC controller 193 may be configured according to a hybrid PF scheme, eg, as described below.

[0896] In some demonstrative aspects, the hybrid PF scheme may be configured to utilize at least one NN-based noise prediction filter and / or at least one NN-based residual noise prediction filter, eg, as described below.

[0897] In some demonstrative aspects, the NN-based noise prediction filter may be configured to be applied to a prediction filter input, which may be based on noise input 104 , eg, as described below.

[0898] In some illustrative aspects, the NN-based residual noise prediction filter can be configured to be applied to a prediction filter input that can be based on acoustic sensor information at one or more acoustic sensing locations, e.g., as described below. For example, the acoustic sensor information at one or more acoustic sensing locations can include residual noise input 106 at residual noise sensing location 107 and / or monitoring input at monitoring location 103, e.g., as described above.

[0899] Reference now Fig.17 , which schematically illustrates a controller 1700 according to some illustrative aspects. For example, the controller 193 ( Figure 1 ) may include one or more elements of the controller 1700, and / or may perform one or more operations and / or functions of the controller 1700.

[0900] In some demonstrative aspects, controller 1700 may be configured according to a hybrid PF scheme.

[0901] In some illustrative aspects, such as Fig.17 As shown, the controller 1700 may include at least one of a NN-based estimator 1710 and / or a NN-based estimator 1720, for example, as described below.

[0902] In some illustrative aspects, such as Fig.17As shown, the NN-based estimator 1710 may include a NN-based noise estimator to be applied to a NN-based estimator input 1712, which may be based on, for example, a noise input 1716 from one or more acoustic sensors 1718 (“reference microphones”). For example, the NN-based estimator input 1712 may be based on the noise input 104 ( Figure 1 ).

[0903] In some illustrative aspects, the NN-based estimator 1720 may include a residual noise-based estimator to be applied to an NN-based estimator input 1722, which may be based on a residual noise input 1726. For example, the residual noise input 1726 may include acoustic sensor information at one or more acoustic sensing locations. For example, the residual noise input 1726 may represent residual noise sensed at one or more residual noise sensing locations, for example, by one or more residual noise sensors 1728 (“error microphones”), e.g., as described above. For example, the NN-based estimator input 1722 may be based on the residual noise input 106 ( Figure 1 For example, the NN-based estimator 1710 and / or the NN-based estimator 1720 may be configured to perform a NN-based PF (eg, the NN-based PF 152 ( Figure 1 ) and / or NN-based PF 452 ( Figure 4 ))'s one or more operations and / or functions as described above.

[0904] In some illustrative aspects, input 1726 may include at least one virtual microphone input corresponding to residual noise (“noise error”) sensed by at least one virtual error sensor at a virtual sensing location. For example, controller 1700 may evaluate the noise error at the virtual sensing location based on input 1726 and predicted noise signal 1729.

[0905] In some illustrative aspects, such as Fig.17 As shown, the controller 1700 can generate a sound control signal 1729 based on the output of the prediction unit 1710 and the output of the NN-based estimator 1720, and can output the sound control signal 1729 to the acoustic transducer 1708.

[0906] In some illustrative aspects, controller 1700 may generate a sound control signal 1729 that is configured to reduce and / or eliminate noise energy and / or amplitude of one or more acoustic modes within a sound control zone, while noise energy and / or amplitude of one or more other acoustic modes may not be affected within the sound control zone, for example, as described below.

[0907] In some illustrative aspects, for example, Fig.17 As shown, the controller 1700 may include an extractor 1714 to extract multiple reference acoustic modes, such as non-intersecting reference acoustic modes, from the input 1716. According to these aspects, the NN-based estimator input 1712 may include multiple reference acoustic modes, such as non-intersecting reference acoustic modes. In other aspects, the extractor 1714 may not be included, and the NN-based estimator input 1712 may be generated directly or indirectly based on the input 1716, for example, according to any other algorithm and / or calculation.

[0908] In some illustrative aspects, for example, Fig.17 As shown, the controller 1700 may include an extractor 1724 to extract multiple residual noise acoustic modes, e.g., non-intersecting residual noise acoustic modes, from the input 1726. According to these aspects, the NN-based estimator input 1722 may include multiple residual noise acoustic modes, e.g., non-intersecting residual noise acoustic modes. In other aspects, the extractor 1724 may not be included, and the NN-based estimator input 1722 may be generated directly or indirectly based on the input 1726, e.g., according to any other algorithm and / or calculation.

[0909] In some illustrative aspects, such as Fig.17 As shown, the controller 1700 may include an echo processing component ("echo canceller") 1715 that is configured to partially or completely reduce, remove and / or eliminate a portion of the signal generated by the speaker 1708 from the reference acoustic information 1716 of the reference microphone 1718, for example, as described above.

[0910] In some demonstrative aspects, echo canceller 1715 may include an AFB mitigation NN, such as AFB NN 155 ( Figure 1 ), the AFB mitigation NN can be trained to generate EC coefficients for the echo canceller 1715, for example, based on the AAC information 1732, for example, as described above.

[0911] In some illustrative aspects, such as Fig.17 As shown, the controller 1700 may include an echo processing component (“echo canceller”) 1725 that is configured to reduce, remove and / or partially or completely cancel a portion of a signal generated by the speaker 1708 from acoustic information 1726 (e.g., at the output signal of the residual noise microphone 1728), for example, as described above.

[0912] In some illustrative aspects, echo canceller 1725 may include an AFB mitigation NN, such as AFB NN 155 ( Figure 1), the AFB mitigation NN can be trained to generate EC coefficients for the echo canceller 1725, for example, based on the AAC information 1732, for example, as described above.

[0913] In some illustrative aspects, controller 1700 may be configured to determine, update, and / or adjust, e.g., in real time, a setting of at least one acoustic mode extractor parameter of extractor 1714 and / or extractor 1724, e.g., based on AAC information 1732. For example, extractor 1714 may be configured to determine, e.g., based on the acoustic mode extractor parameter settings (based on AAC information 1732), a plurality of reference acoustic modes, e.g., disjoint reference acoustic modes, of input 1712. For example, extractor 1724 may be configured to determine, e.g., based on the acoustic mode extractor parameter settings (based on AAC information 1732), a plurality of residual noise acoustic modes, e.g., disjoint residual noise acoustic modes, of input 1722.

[0914] refer to Fig.18 , which schematically illustrates a vehicle 1800 including an AAC system, according to some illustrative aspects.

[0915] In one example, the vehicle 1840 may include the AAC system 100 ( Figure 1 ) that may be configured, for example, to control sound within one or more sound control zones within the vehicle 1800.

[0916] In some illustrative aspects, such as Fig.18 As shown, vehicle 1800 may include multiple speakers 1808 , multiple reference sensors (“ambient microphones”) 1810 , and multiple residual noise sensors (“monitoring microphones”) 1812 .

[0917] In some illustrative aspects, vehicle 1800 may include AAC controller 102 ( Figure 1 ) is configured to control multiple speakers 1808 to provide a first sound control zone 1830 for a driver of the vehicle 1800, for example at the headrest position of the driver's seat.

[0918] In some illustrative aspects, AAC controller 102 ( Figure 1 ) can be configured to control multiple speakers 1808, for example to provide a second sound control zone 1826 for a passenger, such as in a front seat near the driver's seat, such as in the headrest position of a passenger seat.

[0919] In some illustrative aspects, such as Fig.18 As shown, multiple monitoring microphones 1812 can be located within the first and / or second sound control zones 1830 and 1826.

[0920] In some illustrative aspects, such as Fig.18 As shown, multiple ambient microphones 1810 may be located in the environment outside of sound control zones 1830 and 1826 .

[0921] In other aspects, the vehicle 1800 may include any other number of multiple speakers 1808, multiple monitoring microphones 1812, and / or multiple ambient microphones 1810, any other arrangement, positioning, and / or location of multiple speakers 1808, multiple monitoring microphones 1812, and / or multiple ambient microphones 1810, and / or any other additional or alternative components.

[0922] refer to Fig.19 , which shows a method of NN-based AAC. For example, it can be performed by one or more of the following components: Fig.19 One or more operations of: AAC system, for example, AAC system 100 ( Figure 1 ), AA system 400( Figure 4 )、AAC System 700( Figure 7 )、AAC System 1000( Fig.10 ) and / or AAC system 1200 ( Fig.12 ), controllers, for example, controller 102 ( Figure 1 )、Controller 193( Figure 1 ), controller and / or 1400( Fig.14A )、MIMO prediction unit 1500( Fig.15 )、Controller 1600( Fig.16 ) and / or controller 1700( Figure 7 ).

[0923] In some illustrative aspects, as shown at block 1902, the method may include processing input information including active acoustic control (AAC) configuration information and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations in a vehicle, for example. For example, controller 193 ( Figure 1 ) can be configured to process noise input 104 ( Figure 1 ) and / or AAC input 129 ( Figure 1 ) input information 195( Figure 1 ), for example, as described above.

[0924] In some illustrative aspects, as shown at block 1904, the method may include determining a sound control mode for controlling sound within the sound control zone based on the plurality of noise inputs. For example, the sound control mode may be based on a NN output of a NN trained to generate a NN output based on a NN input. For example, the NN input may be based on AAC configuration information. For example, controller 193( Figure 1) can be configured, for example, by using NN 150 ( Figure 1 ) to determine the sound control mode based on the multiple noise inputs 104 ( Figure 1 ) to determine the sound control mode, the NN output can be trained to include AAC configuration information 129 ( Figure 1 )’s NN input to generate NN output, for example, as described above.

[0925] In some illustrative aspects, as shown in block 1906, the method may include outputting the sound control pattern to one or more acoustic transducers. For example, the controller 193 ( Figure 1 ) can be configured to output a sound control signal 109 ( Figure 1 ) to control the acoustic transducer 108 ( Figure 1 ) produces a sound control mode, for example, as described above.

[0926] refer to Fig. 20 , which schematically illustrates an article of manufacture 2000 according to some illustrative aspects. Article 2000 may include one or more tangible computer-readable ("machine-readable") non-transitory storage media 2002, which may include computer-executable instructions, such as implemented by logic 2004, which, when executed by at least one processor, such as a computer processor, may be operable to cause the at least one processor to implement AAC system 100 ( Figure 1 )、Controller 102( Figure 1 )、Controller 193( Figure 1 )、NN 150( Figure 1 )、AAC System 400( Figure 4 )、AAC System 700( Figure 7 )、AAC System 1000( Fig.10 )、AAC system 1200( Fig.12 )、Controller 1400( Fig.14A )、MIMO prediction unit 1500( Fig.15 ) and / or controller 1700( Figure 7 ) to initiate, trigger and / or control the AAC system 100 ( Figure 1 )、Controller 102( Figure 1 )、Controller 193( Figure 1 )、NN 150( Figure 1 )、AAC System 400( Figure 4 )、AAC System 700( Figure 7 )、AAC System 1000( Fig.10 )、AAC system 1200( Fig.12 )、Controller 1400( Fig.14A )、MIMO prediction unit 1500( Fig.15 ) and / or controller 1700( Figure 7 ) performs one or more operations, and / or executes, triggers and / or implements the above reference Figures 1 to 19 One or more operations, communications, and / or functions described, and / or one or more operations described herein. The phrases "non-transitory machine-readable medium" and "computer-readable non-transitory storage medium" may be intended to include all machine and / or computer-readable media, with the only exception being transitory propagating signals.

[0927] In some illustrative aspects, product 2000 and / or computer-readable storage medium 2002 may include one or more types of computer-readable storage media capable of storing data, including volatile memory, non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writable or non-writable memory, etc. For example, computer-readable storage medium 2002 may include RAM, DRAM, double data rate DRAM (DDR-DRAM), SDRAM, static RAM (SRAM), ROM, programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory (e.g., NOR or NAND flash), content addressable memory (CAM), polymer memory, phase change memory, ferroelectric memory, silicon nitride oxide silicon (SONOS) memory, disk, hard drive, etc. Computer-readable storage media may include any suitable media related to downloading or transferring a computer program from a remote computer to a requesting computer carried by a data signal embodied in a carrier wave or other propagation medium over a communication link (e.g., a modem, radio, or network connection).

[0928] In some illustrative aspects, logic 2004 may include instructions, data, and / or code that, if executed by a machine, may cause the machine to perform methods, processes, and / or operations as described herein. The machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, etc., and may be implemented using any suitable combination of hardware, software, firmware, etc.

[0929] In some illustrative aspects, logic 2004 may include or may be implemented as software, software modules, applications, programs, subroutines, instructions, instruction sets, computing codes, words, values, symbols, etc. Instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. Instructions may be implemented according to a predefined computer language, manner, or syntax for instructing a processor to perform a specific function. These instructions may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled, and / or interpreted programming language, machine code, etc.

[0930] Example

[0931] The following examples cover other areas.

[0932] Example 1 includes a device comprising an input for receiving input information, the input information comprising: active acoustic control (AAC) configuration information; and multiple noise inputs, the multiple noise inputs representing acoustic noise at multiple noise sensing locations; a controller, the controller comprising logic and circuitry, the controller configured to process the input information based on the multiple noise inputs to determine a sound control pattern for controlling sounds within a sound control area, wherein the controller comprises a neural network (NN) trained to generate a NN output based on a NN input, wherein the NN input is based on the AAC configuration information, wherein the controller is configured to generate the sound control pattern based on the NN output; and an output end, the output end outputting the sound control pattern to one or more acoustic transducers.

[0933] Example 2 includes the subject matter of Example 1, wherein the NN includes a predictive filter (PF) NN, the PF NN is trained to generate the NN output including a PF setting based on the NN input, wherein the controller is configured to generate the sound control mode by applying a PF configured according to the PF setting to noise information, wherein the noise information is based on the multiple noise inputs.

[0934] Example 3 includes the subject matter of Example 2, and optionally, wherein the PF setting comprises a plurality of PF coefficients.

[0935] Example 4 includes the subject matter of Example 2 or 3, and optionally, wherein the controller includes a parameter extractor configured to determine extracted AAC parameter information based on sensor information from one or more sensors, wherein the NN input includes the extracted AAC parameter information.

[0936] Example 5 includes the subject matter of Example 4, and optionally, wherein the parameter extractor comprises a principal component analysis (PCA) extractor configured to determine the extracted AAC parameter information based on a PCA of the sensor information.

[0937] Example 6 includes the subject matter of Example 4 or 5, and optionally, wherein the sensor information includes at least one of: noise sensor information from one or more noise sensors, residual noise sensor information from one or more residual noise sensors, monitoring sensor information from one or more monitoring sensors, or virtual sensor information from one or more virtual sensors.

[0938] Example 7 includes the subject matter according to any one of Examples 4 to 6, and optionally, wherein the extracted AAC parameter information includes; at least one of road type information, cabin state information, passenger position information, vehicle system state information, noise characteristic information or noise state information, wherein the road type information includes information corresponding to the type of road on which the vehicle including the sound control zone is traveling, wherein the cabin state information includes information corresponding to the state of the cabin of the vehicle, wherein the passenger position information includes information corresponding to the positions of one or more passengers in the cabin of the vehicle, wherein the vehicle system state information includes information corresponding to the state of a system of the vehicle, wherein the noise characteristic information includes information representing one or more characteristics of noise to be mitigated, and wherein the noise state information includes information corresponding to the state of noise to be mitigated.

[0939] Example 8 includes the subject matter of any of Examples 2 to 7, and optionally, wherein the controller is configured to train the PF NN based on noise errors at one or more error sensing locations.

[0940] Example 9 includes the subject matter of any of Examples 2 to 8, and optionally, wherein the controller configures the PF NN based on a transducer transfer function (TTF) setting of a TTF between the one or more acoustic transducers and one or more acoustic sensing locations.

[0941] Example 10 includes the subject matter of Example 9, and optionally, wherein the one or more acoustic sensing locations include at least one of a residual noise sensing location within the sound control zone, a monitoring sensing location, or a noise sensing location outside the sound control zone.

[0942] Example 11 includes the subject matter of any one of Examples 2 to 10, and optionally, wherein the controller includes a transducer transfer function (TTF) NN, the TTF NN trained to determine a TTF setting based on the AAC configuration information, wherein the TTF setting includes a setting of the TTF between the one or more acoustic transducers and one or more acoustic sensing locations, and wherein the controller configures the PF NN based on the TTF setting.

[0943] Example 12 includes the subject matter of Example 11, and optionally, wherein the one or more acoustic sensing locations include at least one of a residual noise sensing location within the sound control zone, a monitoring sensing location outside the sound control zone, or a noise sensing location outside the sound control zone.

[0944] Example 13 includes the subject matter of Example 11 or 12, and optionally, wherein the controller is configured to train the TTF NN based on sensed acoustic information corresponding to the one or more acoustic sensing locations.

[0945] Example 14 includes the subject matter of any one of Examples 2 to 13, and optionally, wherein the input information includes noise error information representing noise errors at one or more error sensing locations, and wherein the controller is configured to determine the noise information based on the noise error information.

[0946] Example 15 includes the subject matter of any one of Examples 1 to 14, and optionally, wherein the controller includes a parameter extractor configured to determine extracted AAC parameter information based on the AAC configuration information, wherein the NN input includes the extracted AAC parameter information.

[0947] Example 16 includes the subject matter of any one of Examples 1 to 15, and optionally, wherein the NN input includes noise error information representative of noise errors at one or more error sensing locations.

[0948] Example 17 includes the subject matter of any one of Examples 1 to 16, and optionally, wherein the input information includes noise error information representing noise error at one or more error sensing locations, wherein the NN input is based on the noise error information.

[0949] Example 18 includes the subject matter of any one of Examples 1 to 17, and optionally, wherein the NN includes an acoustic feedback (AFB) mitigator NN trained to generate a NN output including AFB settings based on the NN input, wherein the controller is configured to determine an AFB mitigation signal by applying the AFB settings to an acoustic transducer signal according to the sound control mode, wherein the controller determines the sound control mode based on the AFB mitigation signal.

[0950] Example 19 includes the subject matter of Example 1, and optionally, wherein the NN is trained to generate the NN output based on the NN input, the NN input comprising the AAC configuration information and noise information based on the multiple noise inputs.

[0951] Example 20 includes the subject matter of any one of Examples 1 to 19, and optionally, wherein the AAC configuration information includes information corresponding to the configuration of AAC within a sound control zone.

[0952] Example 21 includes the subject matter of any one of Examples 1 to 20, and optionally, wherein the AAC configuration information includes information representing a spectral distribution of acoustic signals in at least one of the sound control zone or an environment of the sound control zone.

[0953] Example 22 includes the subject matter of any one of Examples 1 to 21, and optionally, wherein the AAC configuration information includes information representing one or more parameters affecting real-time configuration of the AAC within the sound control zone.

[0954] Example 23 includes the subject matter of any one of Examples 1 to 22, and optionally, wherein the AAC configuration information includes information representing one or more physical characteristics of the sound control zone.

[0955] Example 24 includes the subject matter of any one of Examples 1 to 23, and optionally, wherein the AAC configuration information includes information representative of one or more acoustic characteristics of the sound control zone.

[0956] Example 25 includes the subject matter of any one of Examples 1 to 24, and optionally, wherein the multiple noise inputs are based on noise sensed by one or more acoustic sensors, and wherein the AAC configuration information includes information from one or more information sources different from the one or more acoustic sensors.

[0957] Example 26 includes the subject matter of any one of Examples 1 to 25, and optionally, wherein the multiple noise inputs are based on noise sensed by one or more acoustic sensors, and wherein the AAC configuration information includes information from one or more information sources independent of the one or more acoustic sensors.

[0958] Example 27 includes the subject matter of any one of Examples 1 to 26, and optionally, wherein the AAC configuration information includes vehicle speed information corresponding to a speed of a vehicle including the sound control zone.

[0959] Example 28 includes the subject matter of any one of Examples 1 to 27, and optionally wherein the AAC configuration information includes engine information corresponding to an engine of a vehicle including the sound control zone.

[0960] Example 29 includes the subject matter according to any one of Examples 1 to 28, and optionally, wherein the AAC configuration information includes at least one of the following: braking system information, road detection information, steering information, tire information, seat information, vehicle type information or opening status information, wherein the braking system information includes information corresponding to the braking system of the vehicle including the sound control area, the road detection information includes information from the road detection system of the vehicle, the steering information includes information corresponding to the steering system of the vehicle, the tire information includes information corresponding to one or more tires of the vehicle, the seat position information includes information corresponding to the position or occupancy of one or more seats of the vehicle, the vehicle type information includes information corresponding to the type of the vehicle, and the opening status information includes information corresponding to the opening status of the vehicle.

[0961] Example 30 includes the subject matter of any one of Examples 1 to 29, and optionally, wherein the AAC configuration information includes passenger information corresponding to one or more passengers of a vehicle including the sound control zone.

[0962] Example 31 includes the subject matter of any one of Examples 1 to 30, and optionally, wherein the AAC configuration information includes audio system information corresponding to an audio system of a vehicle including the sound control zone.

[0963] Example 32 includes the subject matter of any one of Examples 1 to 31, and optionally, wherein the AAC configuration information includes climate information corresponding to at least one of a climate within the sound control zone or a climate outside the sound control zone.

[0964] Example 33 includes the subject matter of any one of Examples 1 to 32, and optionally, wherein the AAC configuration information includes user position information corresponding to the position of at least one of a head or an ear of a user within the sound control zone.

[0965] Example 34 includes the subject matter of any one of Examples 1 to 33, and optionally, wherein the AAC configuration information includes user identity information corresponding to an identity of a user to control user preferences regarding the sound control zone.

[0966] Example 35 includes the subject matter of any one of Examples 1 to 34, and optionally, wherein the AAC configuration information includes vehicle system configuration information corresponding to a configuration of an operating mode of one or more vehicle systems of a vehicle including the sound control zone.

[0967] Example 36 includes the subject matter of any one of Examples 1 to 35, and optionally, wherein the AAC configuration information includes vehicle sensor information from one or more vehicle sensors of a vehicle including the sound control zone.

[0968] Example 37 includes the subject matter of any one of Examples 1 to 36, and optionally, wherein the NN is trained to generate the NN output comprising settings of one or more AAC parameters, and wherein the controller is configured to generate the sound control mode based on the settings of the one or more AAC parameters.

[0969] Example 38 includes the subject matter of Example 37, and optionally, wherein the setting of the one or more AAC parameters comprises setting of at least one of a prediction filter or a path transfer function.

[0970] Example 39 includes the subject matter of any one of Examples 1 to 38, and optionally, wherein the input is configured to receive the AAC configuration information via a system bus of a vehicle including the sound control zone.

[0971] Example 40 includes the subject matter of Example 39, and optionally, wherein the input is configured to receive the AAC configuration information via at least one of: Controller Area Network (CAN) bus information received via the vehicle's CAN bus, A2B bus information received via the vehicle's A2B bus, Multimedia Orientation System Transport (MOST) bus information received via the vehicle's MOST bus, wireless communication information received via a wireless communication link, or Ethernet bus information received via the vehicle's Ethernet bus.

[0972] Example 41 includes the subject matter of any one of Examples 1 to 40, and optionally, wherein the NN comprises a deep NN (DNN).

[0973] Example 42 includes an active acoustic control (AAC) system comprising: one or more acoustic transducers; a plurality of noise-sensing acoustic sensors for generating a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations; and a controller for processing input information comprising AAC configuration information and the plurality of noise inputs, wherein the controller determines a sound control mode for controlling sounds within a sound control area based on the plurality of noise inputs, and outputs the sound control mode to the one or more acoustic transducers, wherein the controller comprises a neural network (NN) trained to generate a NN output based on a NN input, wherein the NN input is based on the AAC configuration information, and wherein the controller is configured to generate the sound control mode based on the NN output.

[0974] Example 43 includes an AAC system according to Example 42, and optionally includes a device according to any one of Examples 1 to 41.

[0975] Example 44 includes a vehicle having a plurality of seats and a system according to any of Examples 42-43.

[0976] Example 45 includes a product comprising one or more tangible computer-readable non-transitory storage media, the storage media comprising instructions that, when executed by at least one processor, are operable to enable the at least one processor to enable an active acoustic control (AAC) system to control sound within a sound control zone, the instructions, when executed, enabling the AAC system to: process input information, the input information comprising active acoustic control (AAC) configuration information and multiple noise inputs representing acoustic noise at multiple noise sensing locations; determine a sound control pattern for controlling sound within the sound control zone based on the multiple noise inputs, wherein the sound control pattern is based on a neural network (NN) output of a NN trained to generate the NN output based on an NN input, wherein the NN input is based on the AAC configuration information; and output the sound control pattern to one or more acoustic transducers.

[0977] Example 46 includes the subject matter of Example 45, and optionally, wherein the processor is configured to cause the AAC system to perform one or more operations according to any one of Examples 1 to 41.

[0978] Example 47 includes a system comprising an apparatus according to any one of Examples 1 to 41.

[0979] Example 48 includes an apparatus comprising means for performing any of the operations described in Examples 1-41.

[0980] Example 49 includes a device comprising: a memory interface; and a processing circuit configured to: perform any of the operations described in Examples 1 to 41.

[0981] Example 50 includes a method comprising any of the operations described in Examples 1-41.

[0982] The functions, operations, components, and / or features described herein with reference to one or more aspects may be combined with, or used in combination with, one or more other functions, operations, components, and / or features described herein with reference to one or more other aspects, or vice versa.

[0983] Although certain features have been illustrated and described herein, numerous modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It should therefore be understood that the appended claims are not intended to encompass all such modifications and changes as falling within the true spirit of the disclosure.

Claims

1. A device comprising: Input, the input is used to receive input information, the input information includes: Active Acoustic Control (AAC) configuration information; and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations, and a controller comprising logic and circuitry configured to process the input information based on the plurality of noise inputs to determine a sound control mode for controlling sound within a sound control zone, wherein the controller comprises a neural network (NN) trained to generate NN outputs based on NN inputs, wherein the NN inputs are based on the AAC configuration information, wherein the controller is configured to generate the sound control mode based on the NN outputs; and An output terminal outputs the sound control pattern to one or more acoustic transducers.

2. The apparatus of claim 1 , wherein the NN comprises a predictive filter (PF) NN, the PF NN being trained to generate the NN output comprising a PF setting based on the NN input, wherein the controller is configured to generate the sound control mode by applying a PF configured according to the PF setting to noise information, wherein the noise information is based on the multiple noise inputs.

3. The apparatus of claim 2, wherein the controller comprises a parameter extractor configured to determine extracted AAC parameter information based on sensor information from one or more sensors, wherein the NN input comprises the extracted AAC parameter information. 4 . The apparatus of claim 3 , wherein the parameter extractor comprises a principal component analysis (PCA) extractor configured to determine the extracted AAC parameter information based on a PCA of the sensor information.

5. The apparatus of claim 3, wherein the sensor information comprises at least one of: noise sensor information from one or more noise sensors, residual noise sensor information from one or more residual noise sensors, monitoring sensor information from one or more monitoring sensors, or virtual sensor information from one or more virtual sensors.

6. The apparatus according to claim 3, wherein the extracted AAC parameter information comprises: At least one of road type information, cabin status information, passenger position information, vehicle system status information, noise characteristic information or noise status information, wherein the road type information includes information corresponding to the type of road on which the vehicle including the sound control zone is traveling, wherein the cabin status information includes information corresponding to the status of the cabin of the vehicle, wherein the passenger position information includes information corresponding to the positions of one or more passengers in the cabin of the vehicle, wherein the vehicle system status information includes information corresponding to the status of a system of the vehicle, wherein the noise characteristic information includes information representing one or more characteristics of noise to be mitigated, and wherein the noise status information includes information corresponding to the status of the noise to be mitigated.

7. The apparatus of claim 2, wherein the controller is configured to train the PF NN based on noise errors at one or more error sensing locations.

8. The apparatus of claim 2, wherein the controller configures the PF NN based on a transducer transfer function (TTF) setting of a TTF between the one or more acoustic transducers and one or more acoustic sensing locations.

9. The apparatus of claim 2, wherein the controller comprises a transducer transfer function (TTF) NN, the TTF NN being trained to determine a TTF setting based on the AAC configuration information, wherein the TTF setting comprises a setting of a TTF between the one or more acoustic transducers and one or more acoustic sensing locations, wherein the controller configures the PF NN based on the TTF setting.

10. The apparatus of claim 9, wherein the controller is configured to train the TTF NN based on sensed acoustic information corresponding to the one or more acoustic sensing locations.

11. The apparatus of claim 2, wherein the input information comprises noise error information representing noise errors at one or more error sensing locations, wherein the controller is configured to determine the noise information based on the noise error information.

12. The apparatus of claim 1, wherein the controller comprises a parameter extractor configured to determine extracted AAC parameter information based on the AAC configuration information, wherein the NN input comprises the extracted AAC parameter information.

13. The apparatus of claim 1, wherein the NN input comprises noise error information representative of noise errors at one or more error sensing locations.

14. The apparatus of claim 1 , wherein the NN comprises an acoustic feedback (AFB) mitigator NN trained to generate the NN output comprising AFB settings based on the NN input, wherein the controller is configured to determine an AFB mitigation signal by applying the AFB settings to an acoustic transducer signal according to the sound control mode, wherein the controller determines the sound control mode based on the AFB mitigation signal.

15. The apparatus of claim 1, wherein the NN is trained to generate the NN output based on the NN input, the NN input comprising the AAC configuration information and noise information based on the plurality of noise inputs.

16. The apparatus according to any one of claims 1 to 15, wherein the AAC configuration information comprises information representing a spectral distribution of acoustic signals in at least one of the sound control zone or an environment of the sound control zone.

17. The apparatus of any one of claims 1 to 15, wherein the AAC configuration information comprises information representative of one or more parameters affecting real-time configuration of AAC within the sound control zone.

18. The apparatus of any one of claims 1 to 15, wherein the AAC configuration information includes information representing one or more physical characteristics of the sound control zone.

19. The apparatus according to any one of claims 1 to 15, wherein the AAC configuration information includes information representing one or more acoustic characteristics of the sound control zone.

20. The apparatus according to any one of claims 1 to 15, wherein the NN is trained to generate the NN output comprising settings of one or more AAC parameters, wherein the controller is configured to generate the sound control mode based on the settings of the one or more AAC parameters.

21. An apparatus according to any one of claims 1 to 15, wherein the input is configured to receive the AAC configuration information via a system bus of a vehicle including the sound control zone.

22. An active acoustic control (AAC) system comprising a device according to any one of claims 1 to 15, the AAC system comprising: the one or more acoustic transducers; a plurality of noise sensing acoustic sensors, the plurality of noise sensing acoustic sensors being used to generate the plurality of noise inputs; as well as The controller.

23. An article comprising one or more tangible computer-readable non-transitory storage media, the tangible computer-readable non-transitory storage media comprising instructions operable to, when executed by at least one processor, enable the at least one processor to enable an active acoustic control (AAC) system to control sound within a sound control zone, the instructions, when executed, causing the AAC system to: processing input information including active acoustic control (AAC) configuration information and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations; determining a sound control mode for controlling sound within a sound control zone based on the plurality of noise inputs, wherein the sound control mode is based on a neural network (NN) output, the NN being trained to generate the NN output based on a NN input, wherein the NN input is based on the AAC configuration information; as well as The sound control pattern is output to one or more acoustic transducers.

24. The product of claim 23, wherein the NN comprises a predictive filter (PF) NN, the PF NN being trained to generate the NN output comprising a PF setting based on the NN input, wherein determining the sound control mode comprises determining the sound control mode by applying a PF configured according to the PF setting to noise information, wherein the noise information is based on the plurality of noise inputs.

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