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

Abstract

For example, a controller of an active acoustic control (AAC) system can 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 positions. For example, the controller can be configured to process input information for determining a sound control pattern for controlling sound within a sound control zone based on the plurality of noise inputs. For example, the controller may include a neural network (NN) trained to generate an NN output based on an NN input based on the AAC configuration information. For example, the controller can be configured to generate a sound control pattern based on the NN output and output the sound control pattern to one or more acoustic transducers.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit and priority of 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.

[0002] Aspects described herein generally relate to neural - network(NN) based active acoustic control(AAC).

Background Art

[0003] Active noise control(ANC) is a technology that uses digitally generated noise to reduce unwanted noise. This is based on the principle of superposition of sound waves. Generally, sound is a wave that travels through space. However, if a second sound wave is generated that has the same amplitude as the first sound wave but is in the opposite phase, the first wave can be completely canceled.

[0004] For simplicity and clarity of illustration, the elements shown in the figures are not necessarily drawn to scale. For example, some dimensions of the elements may be exaggerated relative to other elements to clarify the presentation. Further, reference numerals may be repeated throughout the drawings to indicate corresponding or similar elements. The drawings are described below.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0006] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of some embodiments. However, it will be understood by those skilled in the art that some embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, units, and / or circuits are not described in detail so as not to obscure the description.

[0007] Descriptions using terms such as "process", "calculate", "determine", "establish", "analyze", "check", etc. in this specification may refer to operations and / or processes of other electronic computing devices that operate on and / or transform data represented as physical (e.g., electronic) quantities in a computer, computing platform, computing system, or other information storage media that may store instructions for executing operations and / or processes, into other data represented as physical quantities in the registers and / or memories of the computer and / or memories or other information storage media.

[0008] As used herein, the terms "a plurality" and "plural" include, for example, "a number of" or "two or more". For example, "a plurality of articles" includes two or more articles.

[0009] As used herein, the words "exemplary" and "illustrative" are used to mean "serving as an example, instance, illustration, or exemplification". Any aspect, situation, or design described herein as "exemplary" and "illustrative" is not necessarily to be construed as more preferred or advantageous than other aspects, situations, or designs.

[0010] References to "one aspect", "an aspect", "an exemplary aspect", "various aspects", etc. indicate that the aspect(s) so described may include a particular feature, structure, or characteristic, but not all aspects necessarily include that particular feature, structure, or characteristic. Further, repeated use of the phrase "in one aspect" does not necessarily refer to the same aspect, although it may do so.

[0011] As used herein, unless otherwise specified, the use of the ordinal adjectives "first", "second", "third", etc. to describe a common object merely indicates that different examples of similar objects are being referred to, and is not intended to imply that the objects so described must be in a given order, whether temporally, spatially, serially, or in any other manner.

[0012] As used herein, the term "data" can be understood to include any suitable analog or digital form of information provided as a file, a part of a file, a set of files, a signal or stream, a part of a signal or stream, a set of signals or streams, etc. Further, the term "data" can also be used to mean a reference to information, for example, in the form of a pointer. However, the term "data" is not limited to the examples described above and may take various forms and / or represent any information, as understood in the art.

[0013] The terms "processor" or "controller" can be understood to include any suitable type of technical entity that enables the handling of any appropriate type of data and / or information. The data and / or information can be handled according to one or more specific functions executed by the processor or controller. Further, the processor or controller can be understood as any type of circuit, such as any type of analog or digital circuit. Thus, the processor or controller can 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, or any other type of implementation of each function described in more detail hereinafter can also be understood as a processor, controller, or logic circuit. Any two (or more) processors, controllers, or logic circuits detailed herein can be implemented as a single entity having equivalent functions, etc., and conversely, any single processor, controller, or logic circuit detailed herein can be implemented as two (or more) separate entities having equivalent functions, etc., which is understood.

[0014] The term "memory" is understood as a computer-readable medium (e.g., a non-transitory computer-readable medium) in which data or information can be stored for retrieval. Thus, references to "memory" can be understood as references to volatile or non-volatile memory, including random access memory (RAM), read-only memory (ROM), flash memory, solid state storage, magnetic tape, hard disk drives, optical drives, etc., or any combination thereof. Registers, shift registers, processor registers, data buffers, etc. are also included within the term "memory" as used herein. The term "software" can be used to refer to any type of executable instructions and / or logic, including firmware.

[0015] Some portions of the following detailed description 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 are techniques used by those skilled in the data processing arts to convey the substance of their work to others skilled in the art.

[0016] An algorithm is here, and generally, considered to be a self-consistent sequence of acts or operations that lead to a desired result. These include physical manipulations of physical quantities. Although not necessarily, these quantities may often take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has been shown that, mainly for reasons of common usage, it is sometimes convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc. However, it should be understood that all of these terms and similar terms are merely convenient labels associated with appropriate physical quantities and nothing more.

[0017] As used herein, the term "circuit" refers to an application specific integrated circuit (ASIC), integrated circuit, electronic circuit, processor (shared, dedicated, or group), and / or memory (shared, dedicated, or group), combinational logic circuit, and / or other suitable hardware components that perform one or more software or firmware programs, which may be part of or include them. In some embodiments, some of the functions associated with a circuit may be implemented by one or more software or firmware modules. In some embodiments, a circuit may include logic that is at least partially operable in hardware.

[0018] The term "logic" may refer to, for example, computing logic embedded in a circuit of a computing device and / or computing logic stored in a memory of a computing device. For example, the logic may be accessible by a processor of a computing device to perform computing functions and / or operations by executing the computing logic. 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 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, persistent memory, etc. The logic may be executed by one or more processors using memory coupled to the one or more processors that is necessary to execute the logic, such as registers, buffers, stacks, etc.

[0019] Some illustrative embodiments include apparatuses, systems, and methods that can be implemented effectively to control noise, e.g., to reduce or eliminate unwanted noise, e.g., noise in one or more frequency ranges, e.g., generally low, mid, and / or high frequencies, e.g., as described below.

[0020] Some illustrative embodiments may include active acoustic control (AAC) devices, methods, and / or systems configured to control acoustic energy and / or wave amplitude at one or more locations, for example, as described below.

[0021] In some illustrative embodiments, the AAC system may be configured to control the acoustic energy and / or wave amplitude of one or more acoustic patterns generated by one or more acoustic sources, which may include known and / or unknown acoustic sources, for example, as described below.

[0022] In some illustrative embodiments, the AAC system may be configured as and / or perform one or more functions of an active noise control (ANC) system and / or an active sound control (ASC) system that may be configured to control, reshape, reduce, and / or eliminate noise energy and / or wave amplitude of one or more acoustic patterns (“primary patterns”) generated by one or more noise sources, which may include known and / or unknown noise sources, e.g., as described below.

[0023] In some illustrative embodiments, the AAC system may be configured to generate sound control patterns (also referred to as "sound control patterns" or "secondary patterns") including, for example, disruptive noise patterns and / or any other sound control patterns, e.g., as described below.

[0024] In some exemplary embodiments, the AAC system can be configured to generate an acoustic control pattern, for example, based on one or more of a primary pattern, so as to provide, for example, a sound zone, such as a noise reduction zone, such as a quiet zone, controlled by a combination of a secondary pattern and the primary pattern, as will be described hereinafter.

[0025] In some exemplary embodiments, the AAC system can be configured to control, reduce, reform, and / or remove acoustic energy and / or noise within a predetermined position, area, or zone (also referred to as a “sound control zone,” “acoustic control zone,” “noise control zone,” “quiet zone,” and / or “Quiet Bubble (registered trademark)”), as will be described hereinafter.

[0026] In some exemplary embodiments, the AAC system can be configured to control, reduce, reform, and / or remove acoustic energy and / or noise within a sound control zone, for example, without prior information about the primary pattern and / or one or more noise sources, for example, independently of, and / or without using, the prior information.

[0027] For example, the AAC system can be configured to control, reduce, reform, and / or remove acoustic energy and / or noise within an acoustic control zone (sound control zone) independently of, and / or without prior knowledge of, one or more of the one or more noise sources and / or one or more attributes of the primary pattern, such as number, type, location, and / or other attributes of one or more of the primary pattern and / or one or more noise sources, as will be described hereinafter.

[0028] Some example aspects are described herein, for example as described hereinafter, with respect to an AAC system and / or method configured to reform, reduce, and / or remove noise energy and / or wave amplitude of one or more acoustic patterns within a quiet zone.

[0029] However, in other aspects, the AAC and / or sound control system and / or method can be configured to control, in any other way, any other acoustic energy and / or wave amplitude of one or more acoustic patterns within an acoustic control zone (sound control zone) to affect, change, and / or modify the sound energy and / or wave amplitude of one or more acoustic patterns within a predetermined zone, for example as described hereinafter.

[0030] In one example, the AAC system and / or method can be configured to selectively reform, reduce, and / or remove the acoustic energy and / or wave amplitude of one or more types of acoustic patterns within an acoustic control zone (sound control zone), and / or selectively increase and / or amplify the acoustic energy and / or wave amplitude of one or more other types of acoustic patterns within the acoustic control zone, and / or selectively maintain and / or hold the acoustic energy and / or wave amplitude of one or more other types of acoustic patterns within the acoustic control zone, for example as described hereinafter.

[0031] In some example aspects, the AAC system can be configured as a sound control system, for example a personal sound control system (also referred to as a "personal sound bubble (PSB) (registered trademark) system"), that can generate a sound control pattern based on at least one audio input, such that, for example, at least one personal sound zone can be provided based on the audio input, and / or can perform one or more of its functions, for example as described hereinafter.

[0032] In some exemplary embodiments, the AAC system can be configured to control sound within at least one predetermined position, area, or zone, for example, in at least one PSB (registered trademark), based on, for example, audio listened to by a user. In one example, the PSB (registered trademark) can be configured to include an area around the user's head and / or ears, as described hereinafter.

[0033] In some exemplary embodiments, the AAC system can be configured to control the sound contrast between one or more first sound patterns and one or more second sound patterns within the PSB (registered trademark), as described hereinafter.

[0034] In some exemplary embodiments, for example, the AAC system can be configured to control the sound contrast between one or more first sound patterns listened to by a user and one or more second sound patterns, as described hereinafter.

[0035] In some exemplary embodiments, for example, the AAC system can be configured to selectively increase and / or amplify the sound energy and / or wave amplitude of one or more types of acoustic patterns within the PSB (registered trademark) based on, for example, the audio listened to within the PSB (registered trademark), and selectively reform, reduce, and / or remove the sound energy and / or wave amplitude of one or more types of acoustic patterns within the PSB (registered trademark) based on, for example, an acoustic signal that is reduced and / or removed, and / or selectively maintain and / or hold the sound energy and / or wave amplitude of one or more other types of acoustic patterns within the PSB (registered trademark).

[0036] In some exemplary embodiments, the AAC system can be configured to control the sound within the PSB (registered trademark) based on any other additional or alternative input or criterion.

[0037] In some exemplary embodiments, the AAC system may be configured to control, reform, reduce, and / or remove one or more acoustic energies and / or wave amplitudes within a sound control zone of a primary pattern.

[0038] In some exemplary embodiments, the AAC system may be configured to control, reform, reduce, and / or remove noise within a sound control zone in a selective and / or configurable manner, such that, for example, as described hereinafter, the noise energy, wave amplitude, phase, frequency, direction, and / or statistical characteristics of one or more first primary patterns may be affected by a secondary pattern, but the secondary pattern may have little or no effect on the noise energy, wave amplitude, phase, frequency, direction, and / or statistical characteristics of one or more second primary patterns, based on, for example, one or more predetermined noise pattern attributes.

[0039] In some exemplary embodiments, the AAC system may be configured to control, reform, reduce, and / or remove the acoustic energy and / or wave amplitude of a primary pattern within a predetermined enclosure or enclosure that surrounds and / or encloses, for example, an acoustic control zone (sound control zone), and / or at one or more predetermined positions within the acoustic control zone (sound control zone). [[ID=^{}11]]

[0040] In one example, the acoustic control zone (sound control zone) may include a two-dimensional zone that defines an area, for example, where one or more acoustic energies and / or wave amplitudes of a primary pattern are controlled, reformed, reduced, and / or removed.

[0041] According to this example, the AAC system may be configured to control, reform, reduce, and / or remove the acoustic energy and / or wave amplitude of a primary pattern along the perimeter surrounding the acoustic control zone (sound control zone), and / or at one or more predetermined positions within the acoustic control zone (sound control zone).

[0042] In one example, an acoustic control zone (sound control zone) may include a three-dimensional zone that defines a volume in which one or more acoustic energies and / or wave amplitudes of a primary pattern are controlled, reformed, reduced, and / or removed. According to this example, the AAC system may be configured to control, reform, reduce, and / or remove the acoustic energy and / or wave amplitude of the primary pattern on a surface surrounding the three-dimensional volume.

[0043] In one example, the acoustic control zone (sound control zone) may include a spherical volume, and the AAC system may be configured to control, reform, reduce, and / or remove the acoustic energy and / or wave amplitude of the primary pattern on the surface of the spherical volume.

[0044] In another example, the acoustic control zone (sound control zone) may include a cubic volume, and the AAC system may be configured to control, reform, reduce, and / or remove the acoustic energy and / or wave amplitude of the primary pattern on the surface of the cubic volume.

[0045] In other aspects, the acoustic control zone (sound control zone) may include any other suitable volume that may be defined, for example, based on one or more attributes of the location where the acoustic control zone is maintained.

[0046] Now, refer to FIG. 1, which schematically shows the AAC system 100 according to some exemplary aspects.

[0047] Also refer to FIG. 2, which schematically shows the deployment scheme 200 of the components of the AAC system according to some exemplary aspects. For example, the deployment scheme 200 may include the deployment of one or more elements of the AAC system 100 of FIG. 1, for example, some or all of the elements.

[0048] In some exemplary aspects, the AAC system 100 may include, operate as, and / or perform the functions of, for example, as described hereinafter, an AAC system, an active noise cancellation (ANC) system, an acoustic control system, a sound control system, a PSB (registered trademark) system, and / or a Quiet Bubble (registered trademark) system.

[0049] In some exemplary aspects, the AAC system 100 may include a controller 102 (also referred to as an “AAC controller”) for controlling sound in at least one AAC zone 110 (also referred to as a “sound control zone” or an “acoustic control zone”), for example, as described hereinafter.

[0050] In some exemplary aspects, the controller 102 may include circuitry and / or logic, such as one or more processors including circuitry and / or logic, and / or a memory circuit and / or logic, or may be implemented in part or in whole thereby. Additionally or alternatively, one or more functions of the controller 102 may be implemented by logic executable by a machine and / or one or more processors, for example, as described hereinafter.

[0051] In one example, the controller 102 may be configured to store at least a portion of information processed by one or more processors and / or circuitry, and / or logic utilized by the processors and / or circuitry, for example, at least temporarily, and may include at least one memory 198 coupled to one or more processors.

[0052] In one example, at least a portion of the functions of the controller 102 may be implemented by an integrated circuit, such as a chip, such as a system on chip (SoC).

[0053] In other aspects, the controller 102 may be implemented by any other logic and / or circuitry and / or according to any other architecture.

[0054] In some exemplary aspects, the AAC zone 110 may include an enclosed space, as described hereinafter for example.

[0055] In some exemplary aspects, the enclosed space may include a part or all of the passenger compartment of a vehicle, such as a passenger car, a bus, and / or a truck, as described hereinafter for example.

[0056] In some exemplary aspects, the AAC zone 110 may include a part of the space within the passenger compartment of a vehicle.

[0057] In one example, the AAC zone 110 may include a space defined for a human within the vehicle.

[0058] In one example, the AAC zone 110 may include a space defined for a seat within the vehicle.

[0059] For example, the AAC zone 110 may be defined to include the space around the driver's seat of the vehicle, the space around the headrest of the driver's seat of the vehicle, and / or the space around the head and / or ears of the driver of the vehicle.

[0060] For example, another AAC zone 110 may be defined to include the space around another seat of the vehicle, such as the passenger seat or the rear 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.

[0061] In other aspects, the AAC zone 110 may include substantially the entire space of the vehicle cabin.

[0062] In some exemplary aspects, the enclosed space may include any other cabin, such as an aircraft cabin, a train cabin, a medical system cabin, an indoor area, etc.

[0063] In other aspects, the enclosed space may include a sealed portion or area of any other space, such as a vehicle or non-vehicle.

[0064] In some exemplary aspects, the sound control zone 110 may be located inside a vehicle, and the AAC system 100 may be deployed as part of the vehicle. In other aspects, the sound control zone 110 may be located in any area or location outside a vehicle.

[0065] In some exemplary aspects, the sound control zone 110 may include a three-dimensional (3D) zone. For example, the sound control zone 110 may include a spherical zone.

[0066] In another example, the sound control zone 110 may include any other 3D zone.

[0067] In some exemplary aspects, the AAC system 100 may be configured to control the sound and / or noise within the zone 110 in a way that provides, for example, an improved music and / or sound experience within a vehicle, an improved quality of calls, etc., to provide an improved driving experience for the driver and / or one or more passengers of the vehicle.

[0068] In some exemplary aspects, the AAC controller 102 may include an input 191 that may be configured to receive input information 195, as described hereinafter, or may be implemented with the input 191.

[0069] In some exemplary embodiments, the AAC controller 102 may include a controller 193 configured to determine a sound control pattern for controlling sound in at least one sound control zone 110 in a vehicle, for example, based on input information 195 as described hereinafter.

[0070] In some exemplary embodiments, the controller 193 may include circuitry and / or logic, such as one or more processors including circuitry and / or logic, and / or memory circuitry and / or logic, or may be implemented in part or in whole thereby. Additionally or alternatively, one or more functions of the controller 193 may be implemented by logic executable by a machine and / or one or more processors, as described hereinafter.

[0071] In some exemplary embodiments, the input information 195 may include a plurality of noise inputs 104 from a plurality of acoustic sensors (also referred to as, for example, "primary sensors", "noise sensors", or "reference sensors") representing acoustic noise at a plurality of predetermined noise sensing locations 105, as described hereinafter.

[0072] In some exemplary embodiments, the AAC controller 102 may receive noise inputs 104 from one or more acoustic sensors 119 that may include one or more physical sensors located at one or more of the positions 105, indicated as "N", such as microphones, accelerometers, tachometers, etc., and / or one or more virtual sensors configured to estimate acoustic noise at one or more of the positions 105, as described hereinafter.

[0073] In some exemplary embodiments, the noise input 104 may be based on monitoring information sensed by one or more monitoring sensors, indicated as "M", such as microphones, accelerometers, tachometers, etc., at one or more monitoring positions, as described hereinafter.

[0074] In some exemplary embodiments, the noise input 104 may include a noise input corresponding to a virtual sensor at the virtual sensor position 105. For example, the noise input corresponding to the virtual sensor at the virtual sensor position 105 may be based on monitoring information monitored by one or more sensors at one or more monitoring positions 103, as described hereinafter.

[0075] In some exemplary embodiments, the one or more monitoring positions 103 may include one or more positions different from the noise sensing position 105, as described hereinafter.

[0076] In some exemplary embodiments, as shown in FIG. 2, 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 within the sound control zone 110.

[0077] In one example, in a vehicle implementation of the AAC system 100, the one or more monitoring positions 103 may include, for example, a monitoring position within the vehicle cabin, a monitoring position on the vehicle roof, a monitoring position on the vehicle chassis, a monitoring position on the outer surface of the vehicle, a monitoring position on the vehicle wheels, and / or any other additional or alternative positions.

[0078] In some exemplary embodiments, the input information 195 may include a plurality of residual noise inputs 106 from one or more physical and / or virtual residual noise acoustic sensors 121 (also referred to as "error sensors" or "secondary sensors") representing acoustic residual noise at a plurality of predetermined residual noise monitoring positions 107. For example, the residual noise sensing position 107 may be located within the sound control zone 110, as described hereinafter.

[0079] In some exemplary embodiments, the input information 195 may include a plurality of residual noise inputs 106, as described hereinafter.

[0080] In other aspects, one or more of the plurality of residual noise inputs 106, for example some or all, may be optional and / or may be excluded. For example, in some exemplary aspects, the AAC controller 102 may be configured to provide a technical solution for controlling sound within at least one AAC zone 110 without using, for example, one or more of the plurality of residual noise inputs 106, for example some or all, as will be described later.

[0081] In some exemplary aspects, the AAC controller 102 may receive the residual noise input 106 from one or more acoustic sensors 121, for example one or more physical sensors located at one or more of the positions 107, such as microphones, accelerometers, tachometers, etc., and / or from one or more virtual sensors configured to estimate the residual noise at one or more of the positions 107.

[0082] In some exemplary aspects, the residual noise input 104 may include a residual noise input corresponding to a virtual sensor at the virtual sensor position 107. For example, the residual noise input corresponding to the virtual sensor at the virtual sensor position 107 may be based on the monitoring information sensed by one or more sensors at one or more monitoring positions 103, as will be described later, for example.

[0083] In some exemplary aspects, the AAC system 100 may include at least one acoustic transducer 108, such as a speaker, shaker, and / or any other actuator. For example, the AAC controller 102 may control the acoustic transducer 108 to generate an acoustic sound control pattern configured to control the sound within the sound control zone 110, as will be described in detail later, for example.

[0084] In some exemplary 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 the sound control signal 109.

[0085] In some exemplary aspects, at least one acoustic transducer 108 may be placed at one or more positions that may be determined based on one or more attributes of the sound control zone 110, such as the size and / or shape of the zone 110, one or more expected attribute inputs 104, one or more expected attributes of one or more potential actual noise sources 202, such as the expected position and / or directivity of the noise source 202 relative to the sound control zone 110, the number of noise sources 202, etc.

[0086] In one example, the acoustic transducer 108 may include a speaker array or a multi-channel acoustic source that includes a predetermined number of speakers, denoted as Mspk. In some exemplary aspects, the acoustic transducer 108 may include an array of speakers implemented using, for example, suitable "small acoustic sources" placed at suitable positions outside the zone 110.

[0087] In another example, the speaker array may be implemented using, for example, a plurality of speakers distributed in the space around the sound control zone 110.

[0088] In some exemplary aspects, one or more of the positions 105 may be distributed, for example, as described later, at positions on and / or outside a spherical volume, such as any combination of one or more positions surrounding the spherical volume.

[0089] In some exemplary aspects, one or more of the positions 105 may be distributed outside the sound control zone 110. For example, one or more of the positions 105 may be distributed on or proximate to an enclosure or enclosure that surrounds the sound control zone 110.

[0090] For example, if the sound control zone 110 is defined by a spherical volume, one or more of the positions 105 may be distributed on and / or outside the surface of the spherical volume.

[0091] In some exemplary embodiments, one or more of the positions 105 may be distributed at one or more other additional or alternative positions relative to the sound control zone 110. In one example, in a vehicle implementation of the AAC system 100, one or more noise sensing positions 105 may include, for example, noise sensing positions 105 within the vehicle cabin, noise sensing positions 105 on the vehicle chassis, noise sensing positions 105 on the outer surface of the vehicle, noise sensing positions 105 on the vehicle wheels, and / or any other additional or alternative positions.

[0092] In some exemplary embodiments, the position 107 may be distributed within the sound control zone 110. In one example, one or more of the positions 107 may be close to the enclosure of the sound control zone 110. In one example, one or more of the positions 107 may be close to the user's head and / or ears of the sound control zone 110.

[0093] For example, if the zone 110 is defined by a spherical volume, the position 107 may be distributed on a spherical surface having a radius smaller than the radius of the sound control zone 110.

[0094] In some exemplary embodiments, the AAC system 100 may include one or more first acoustic sensors ("primary sensors") 119 to sense acoustic noise at one or more of the plurality of noise sensing positions 105 and / or to sense monitoring acoustic information at one or more of the monitoring positions 103.

[0095] In some exemplary aspects, the AAC system 100 may include one or more second acoustic sensors (“error sensors”) 121 to sense acoustic residual noise at one or more of the plurality of residual noise sensing locations 107, as described hereinafter, for example.

[0096] In other aspects, some or all of the acoustic sensors 121 may be optional or may be excluded, as described hereinafter, for example.

[0097] In some exemplary aspects, one or more of the error sensors and / or one or more of the primary sensors may be implemented using one or more “virtual sensors” (“virtual microphones”). A virtual microphone corresponding to a particular microphone position may be implemented by any suitable algorithm and / or method capable of evaluating the acoustic pattern sensed by an actual acoustic sensor located at the particular microphone position.

[0098] In some exemplary aspects, the AAC controller 102 may be configured to simulate and / or execute the functions of the virtual microphones by, for example, estimating and / or evaluating the acoustic noise pattern at a particular position of the virtual microphones.

[0099] In some exemplary aspects, an AAC system, such as AAC system 100 (FIG. 1), may include a first array 219 of one or more primary sensors configured to sense a primary pattern at one or more of location 105 and / or location 103, such as microphones, accelerometers, tachometers, etc. For example, array 219 may include a plurality of acoustic sensors 119 (FIG. 1). For example, array 219 may include a microphone for outputting a noise signal 104 (FIG. 1) including, for example, a sequence of Nmic samples per second. For example, if the microphone operates at a sampling rate of about 48 KHz, Nmic may be 48,000 samples per second. Noise signal 104 (FIG. 1) may include any other suitable signal having any other suitable sampling rate and / or any other suitable attributes.

[0100] In some exemplary aspects, one or more of the 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 at location 105 may be implemented by any suitable algorithm and / or method capable of evaluating an acoustic pattern sensed by an acoustic sensor located at the particular microphone position, such as as part of controller 102 (FIG. 1) or any other element of system 100 (FIG. 1). For example, controller 102 (FIG. 1) may be configured to evaluate the acoustic pattern of a virtual microphone based on at least one actual acoustic pattern sensed by at least one microphone 119 (FIG. 1) of array 219.

[0101] In some exemplary aspects, AAC controller 102 may be configured to simulate and / or execute the function of a virtual primary sensor at primary sensor location 105 based on monitoring information sensed by one or more monitoring sensors at one or more monitoring locations 103, for example.

[0102] In some exemplary embodiments, the AAC system 100 (FIG. 1) may include one or more error sensors, such as a second array 221 of microphones, configured to sense acoustic residual noise at one or more of positions 107. For example, the array 221 may include a plurality of acoustic sensors 121 (FIG. 1). For example, the error sensors may include one or more sensors for sensing an acoustic residual noise pattern on a spherical surface within the spherical sound control zone 110. In other embodiments, some or all of the acoustic sensors may be optional or may be excluded.

[0103] In some exemplary embodiments, one or more of the sensors of the array 221 may be implemented using one or more “virtual sensors”. For example, the array 221 may include a combination of at least one microphone and at least one virtual microphone. The virtual microphone corresponding to a particular microphone position at position 107 may be implemented by any suitable algorithm and / or method capable of evaluating the acoustic pattern sensed by an acoustic sensor located at the particular microphone position, for example as part of the controller 102 (FIG. 1) or any other element of the system 100 (FIG. 1). For example, the controller 102 (FIG. 1) may be configured to evaluate the acoustic pattern of the virtual microphone based on at least one actual acoustic pattern sensed by at least one microphone 121 (FIG. 1) of the array 221.

[0104] In some exemplary embodiments, the AAC controller 102 may be configured to simulate and / or execute the function of a virtual primary sensor at the error sensor position 107 based on, for example, monitoring information sensed by one or more monitoring sensors at one or more monitoring positions 103.

[0105] In some illustrative embodiments, 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 of locations 103, 105, and 107 may be determined based on the size of sound control zone 110 and / or the enclosure of sound control zone 110, the shape of the enclosure of sound control zone 110 and / or the enclosure of sound control zone 110, one or more attributes of the acoustic sensors located at one or more of locations 103, 105, and / or 107, such as the sampling rate of the sensors, etc.

[0106] In one example, one or more acoustic sensors, eg, microphones, accelerometers, tachometers, etc., may be deployed at locations 103, 105, and / or 107 according to the spatial sampling theorem, eg, as defined by Equation 1 below.

[0107] For example, the number of primary sensors, the distance between the primary sensors, the number of error sensors, and / or the distance between the error sensors may be determined according to the spatial sampling theorem, for example, as defined by Equation 1 below.

[0108] In one example, the primary sensors and / or error sensors may be spaced apart, e.g., evenly or unevenly, at a distance denoted d from each other. For example, the distance d may be determined as follows:

number

[0109] In one example, if the maximum frequency of interest is fmax=100 (Hz), the distance d is d≦(343) / (2 * 100) = 1.7(m).

[0110] As shown in FIG. 2, the deployment scheme 200 is configured for a circular or spherical sound control zone 110. For example, one or more positions 105 are distributed substantially uniformly in a spherical or circular shape, for example, around the sound control zone 110, and the position 107 is distributed substantially uniformly in a spherical or circular shape, for example, within the sound control zone 110.

[0111] However, in other embodiments, the components of the AAC system 100 may be deployed according to any other deployment scheme, including any suitable distribution of positions 103, 105, and / or 107, configured for a sound control zone of any other suitable form and / or shape.

[0112] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control pattern according to at least one acoustic parameter, such as energy, amplitude, phase, frequency, direction, and / or statistical characteristics within the sound control zone 110, as described in detail later.

[0113] In some exemplary embodiments, the AAC controller 102 may determine a sound control pattern to selectively reduce one or more predetermined first acoustic patterns, such as a noise pattern, within the sound control zone 110 and not reduce one or more second acoustic patterns, such as a noise pattern, within the sound control zone 110, as described later.

[0114] In some exemplary aspects, the sound control zone 110 may be located inside the vehicle, and the AAC controller 102 may selectively reduce one or more first acoustic patterns including, for example, a road noise pattern, a wind noise pattern, and / or an engine noise pattern, and not reduce one or more second acoustic patterns including, for example, an audio pattern of an audio device located within the vehicle, a siren noise pattern, a horn noise pattern, a hazard acoustic pattern of a hazard, an alarm acoustic pattern of an alarm signal, an acoustic pattern of an information signal, etc., so as to determine a sound control pattern.

[0115] In other aspects, the sound control zone 110 may be, for example, at any other location and / or area inside or outside the vehicle, and the AAC controller 102 may be configured to determine a sound control pattern so as to selectively reduce any other one or more first acoustic patterns while not substantially reducing or affecting any other one or more second acoustic patterns.

[0116] In some exemplary aspects, the AAC controller 102 may be configured to determine a sound control pattern even without having information regarding one or more noise source attributes of one or more actual noise sources 202 that generate acoustic noise, for example, at the noise sensing location 105.

[0117] For example, the noise source attributes may include the number of noise sources 202, the location of the noise sources 202, the type of the noise sources 202, and / or one or more attributes and / or characteristics of one or more noise patterns generated by one or more of the noise sources 202.

[0118] In some illustrative embodiments, the AAC controller 102 may be configured to determine the sound control pattern, for example, taking into account one or more factors, for example, one or more acoustic transfer functions between elements of the AAC system 100, for example, an acoustic transfer function between at least one acoustic transducer 108 and one or more acoustic sensing locations, as described below.

[0119] In some illustrative embodiments, an acoustic transfer function may represent and / or describe the acoustic medium through which sound waves pass. For example, the transfer function between an origination point and a destination point may include a direct path defined by a straight line connecting the origination point and the destination point (if any), and / or one or more multi-paths, such as indirect paths including reflections from objects in the environment surrounding the origination point and the destination point.

[0120] In some illustrative embodiments, 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 an acoustic transducer 108 and an acoustic sensor physically located at the acoustic sensing location, e.g., as described below.

[0121] In some demonstrative embodiments, the one or more acoustic sensing locations may include a virtual sensing location of a virtual acoustic sensor. For example, the one or more acoustic transfer functions may include an acoustic transfer function between the acoustic transducer 108 and a virtual acoustic sensor at the virtual acoustic sensing location, e.g., as described below.

[0122] In some illustrative embodiments, the one or more acoustic sensing locations may include a residual noise sensor 121, e.g., a physical residual noise sensor 121 or a virtual residual noise sensor 121, at a residual noise sensing location 107, 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 the residual noise sensing location 107, e.g., as described below.

[0123] In some exemplary embodiments, one or more acoustic sensing locations may include, for example as described hereinafter, the noise sensing location 105 of a noise sensor 119, such as a physical noise sensor 119 or a virtual noise sensor 119. For example, one or more acoustic transfer functions may include, for example as described hereinafter, the acoustic transfer function between an acoustic transducer 108 and the noise sensing location 105.

[0124] In some exemplary embodiments, one or more acoustic sensing locations may include, for example as described hereinafter, the monitoring sensing location 103 of a monitoring sensor 119. For example, one or more acoustic transfer functions may include, for example as described hereinafter, the acoustic transfer function between an acoustic transducer 108 and the monitoring sensing location at the monitoring location 103.

[0125] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control pattern, for example as described hereinafter, taking into account, for example, the acoustic statistical characteristics of an acoustic signal, such as noise, handled by the AAC system 100.

[0126] In some exemplary embodiments, the statistical characteristics of an acoustic signal, such as noise, handled by the AAC system 100 may be based on the spectral distribution of the acoustic signal, such as how the energy of the acoustic signal is distributed over a suitable frequency range.

[0127] In other embodiments, the AAC controller 102 may be configured to determine a sound control pattern based on any other additional or alternative factors, criteria, attributes, and / or parameters.

[0128] In some exemplary embodiments, the acoustic transfer functions within and / or in the environment of the sound control zone 110 are susceptible to the influence of physical changes in the environment of the sound control zone 110.

[0129] In some exemplary embodiments, the acoustic transfer function in the vehicle environment is susceptible to the effects of physical changes in the vehicle environment, such as, for example, the position and / or angle of the vehicle seat, the number of passengers in the vehicle, one or more open / closed windows, and / or any other additional or alternative attributes of the vehicle environment, as will be described hereinafter.

[0130] In some exemplary embodiments, the spectral distribution of an acoustic signal, such as a noise signal, in the vehicle environment can be affected by one or more factors, such as, for example, the road surface, the type of vehicle tires, the speed of the vehicle, the engine speed (RPM) of the vehicle, wind-induced noise, the operation of the air conditioning system in the vehicle, and / or one or more additional or alternative factors, as will be described hereinafter.

[0131] In some exemplary embodiments, the AAC controller 102 can be configured to determine and / or set a sound control pattern, for example, based on a predetermined transfer function and / or the spectral distribution of noise, to set the operation of the AAC system 100 to predetermined conditions, as will be described hereinafter.

[0132] In some exemplary embodiments, the AAC controller 102 can be configured to determine and / or set a sound control pattern, for example, based on a current or real-time estimated transfer function and / or the spectral distribution of noise, to set the operation of the AAC system 100 to current or real-time conditions, as will be described hereinafter.

[0133] In some exemplary embodiments, the AAC controller 102 can be configured to determine and / or set a sound control pattern, for example, based on one or more changes in the transfer function and / or the spectral distribution of noise, to set the operation of the AAC system 100 to new conditions.

[0134] In some exemplary aspects, the AAC controller 102 can be configured to set the parameters of the AAC system 100 in real time and / or continuously in a way that can address, for example, one or more technical problems, as will be described later.

[0135] In some exemplary aspects, the AAC controller 102 can include and / or be configured to execute one or more operations and / or functions of a state machine that can receive inputs from, for example, one or more information sources within a vehicle computer and / or one or more detectors to monitor, for example, one or more environmental conditions, as will be described later.

[0136] In one example, in a vehicle implementation, inputs from one or more information sources can include information indicating, for example, the position of the vehicle seat, the number of passengers, the speed of the vehicle, the engine speed, etc., as will be described later.

[0137] In another example, inputs from one or more information sources can include information indicating the environment of the sound control zone 110, for example, the temperature and / or pressure in the vehicle cabin.

[0138] In some exemplary aspects, the AAC controller 102 can be configured to determine the operating mode of the AAC system 100, for example, by programming the AAC system 100 with an appropriate parameter set, as will be described later.

[0139] In some exemplary aspects, the input information 195 can include AAC information 129 that can be received from one or more information sources 120 (also referred to as "AAC support information", "AAC assistance information", or "AAC configuration information").

[0140] For example, in a vehicle implementation, the information source 120 can include one or more information sources within the vehicle, as will be described later.

[0141] In one example, the AAC information may include environmental information corresponding to the environment of the vehicle.

[0142] In another example, the AAC information may include cabin information corresponding to the cabin of the vehicle.

[0143] In another example, the AAC information may include vehicle information corresponding to the physical parameters of the vehicle.

[0144] In other aspects, the AAC information may include any other additional and / or alternative information of one or more parameters affecting the sound control zone 110 and / or information corresponding to the environment of the sound control zone 110.

[0145] In some exemplary aspects, the controller 193 may be configured to receive and process the AAC information 129 via, for example, the input 191, as will be described later.

[0146] In some exemplary aspects, the controller 193 may be configured to determine the sound control signal 109 based on, for example, the AAC information 129 in addition to, for example, the noise input 104 and / or the residual noise input 106, as will be described later.

[0147] In some exemplary aspects, the AAC information 129 may include information corresponding to the configuration of AAC within the sound control zone 110 and / or one or more other sound control zones, as will be described later.

[0148] In some exemplary aspects, the AAC information 129 may include information of one or more parameters and / or attributes affecting the AAC configuration corresponding to the sound control zone 110, as will be described later.

[0149] In some exemplary aspects, the AAC configuration information 129 may include information representing, for example, as described later, the spectral distribution of the acoustic signal within and / or in the environment of the sound control zone 110.

[0150] In some exemplary aspects, the AAC configuration information 129 may include information representing one or more parameters that affect the real-time configuration of the AAC within the sound control zone 110, for example, as described later.

[0151] In some exemplary aspects, the AAC configuration information 129 may include information representing one or more physical characteristics of the sound control zone 110, for example, as described later.

[0152] In some exemplary aspects, the AAC configuration information 129 may include information representing one or more acoustic characteristics of the sound control zone 110, for example, as described later.

[0153] In some exemplary aspects, the AAC configuration information 129 may include information from one or more acoustic sensors of the system 100, such as the noise sensor 119, the residual noise sensor 121, and / or one or more information sources 120 different from the monitoring sensors.

[0154] In some exemplary aspects, the AAC configuration information 129 may include information from one or more information sources 120 independent of one or more acoustic sensors of the system 100, such as the noise sensor 119, the residual noise sensor 121, and / or the monitoring sensors.

[0155] In some exemplary aspects, the AAC support information 129 may include information that can be utilized by the AAC controller 193 to support the AAC controller 193, for example, as described later, in the configuration of, for example, one or more AAC settings and / or AAC parameters.

[0156] In some exemplary embodiments, the AAC support information 129 may include real-time input information received in real time from one or more information sources 120, for example, as described later, for example, during operation of the AAC system 100.

[0157] In some exemplary embodiments, the AAC configuration information 129 may include real-time information corresponding to the real-time acoustic configuration of the sound control zone 110 and / or other sound control zones, for example, as described later.

[0158] In some exemplary embodiments, the AAC information 129 may include information corresponding to, representing, and / or affecting one or more sound control parameters of the sound control settings of the sound control zone 110, for example, as described later.

[0159] In some exemplary embodiments, the AAC information 129 may include acoustic configuration information corresponding to the acoustic configuration of the sound control zone 110, for example, as described later.

[0160] In some exemplary embodiments, the AAC configuration information 129 may include information representing one or more parameters that affect the real-time configuration of AAC within the sound control zone 110, for example, as described later.

[0161] In some exemplary embodiments, the AAC configuration information 129 may be different from, for example, as described later, the plurality of noise inputs 104.

[0162] In some exemplary embodiments, the AAC configuration information 129 may be received separately from, for example, as described later, the plurality of noise inputs 104.

[0163] In some exemplary embodiments, the AAC configuration information 129 may be received from one or more information sources 120 that are separate from and / or may be different from the reference noise sensor 119, for example, as described later.

[0164] In some exemplary embodiments, the AAC support information 129 may include, for example as described hereinafter, acoustic configuration information including information related to one or more parameters of the acoustic configuration of the sound control zone 110.

[0165] In some exemplary embodiments, the AAC information 129 may include, for example as described hereinafter, acoustic configuration information including information defining one or more parameters of the acoustic configuration of the sound control zone 110.

[0166] In some exemplary embodiments, the AAC information 129 may include, for example as described hereinafter, acoustic configuration information including information affecting one or more parameters of the acoustic configuration of the sound control zone 110.

[0167] In some exemplary embodiments, the AAC support information 129 may include, for example as described hereinafter, acoustic configuration information including information representing one or more parameters of the acoustic configuration of the sound control zone 110.

[0168] In some exemplary embodiments, the AAC support information 129 may include, for example as described hereinafter, information corresponding to an AAC configuration that affects the sound control zone 110 installed in the vehicle.

[0169] In some exemplary embodiments, the AAC support information 129 may include, for example as described hereinafter, vehicle system configuration information corresponding to the configuration of the operating mode of one or more vehicle systems of the vehicle including the sound control zone 110.

[0170] In some exemplary embodiments, the AAC support information 129 may include, for example as described hereinafter, vehicle type information corresponding to the type of the vehicle including the sound control zone 110.

[0171] In some exemplary embodiments, the AAC assistance information 129 may include vehicle sensor information from one or more vehicle sensors of a vehicle including the sound control zone 110, for example as described later.

[0172] In some exemplary embodiments, the AAC assistance information 129 may include vehicle speed information corresponding to the speed of a vehicle including the sound control zone 110, for example as described later.

[0173] In some exemplary embodiments, the AAC assistance information 129 may include engine information corresponding to the engine of a vehicle including the sound control zone 110, for example as described later.

[0174] In some exemplary embodiments, the AAC assistance information 129 may include brake system information corresponding to the brake system of a vehicle including the sound control zone 110, for example as described later.

[0175] In some exemplary embodiments, the AAC assistance information 129 may include road detection information from a road detection system of a vehicle including the sound control zone 110, for example as described later.

[0176] In some exemplary embodiments, the AAC assistance information 129 may include steering information corresponding to the steering system of a vehicle including the sound control zone 110, for example as described later.

[0177] In some exemplary embodiments, the AAC assistance information 129 may include tire information corresponding to one or more tires of a vehicle including the sound control zone 110, for example as described later.

[0178] In some exemplary embodiments, the AAC assistance information 129 may include seat position information corresponding to one or more seats of a vehicle including the sound control zone 110, for example as described later.

[0179] In some exemplary embodiments, the AAC support information 129 may include passenger information corresponding to one or more passengers in a vehicle including the sound control zone 110, for example, as described later.

[0180] In some exemplary embodiments, the AAC support information 129 may include opening state information corresponding to the state of an opening of a vehicle including the sound control zone 110, for example, as described later.

[0181] In some exemplary embodiments, the AAC support information 129 may include audio system information corresponding to the audio system of a vehicle including the sound control zone 110, for example, as described later.

[0182] In some exemplary embodiments, the AAC support information 129 may include climate information corresponding to at least one of the climate inside the sound control zone 110 or the climate outside the sound control zone 110, for example, as described later.

[0183] In some exemplary embodiments, the AAC support information 129 may include user position information corresponding to the position of at least one of the user's head or ears inside the sound control zone 110, for example, as described later.

[0184] In some exemplary embodiments, the AAC support information 129 may include user identity information corresponding to the user's identity to control the user preferences for the sound control zone 110, for example, as described later.

[0185] In one example, the AAC support information 129 may include user identity information corresponding to the identity of the user of the sound control zone 110. For example, the AAC support information 129 may include user identity information corresponding to the identity of the driver of the vehicle to control the user preferences for the sound control zone 110 implemented, for example, with respect to the driver's seat of the vehicle.

[0186] In another example, the AAC support information 129 may include user identity information corresponding to a user identity to control user preferences for a sound control zone 110 that can be used by another user. For example, the AAC support information 129 may include user identity information corresponding to the identity of a vehicle driver to control user preferences for a sound control zone 110 implemented with respect to one or more passenger seats of a vehicle.

[0187] In some exemplary aspects, the AAC support information 129 may include acoustic configuration information including any other optional additional or alternative information that may be related to, for example, the acoustic configuration of the sound control zone 110 as described hereinafter.

[0188] In some exemplary aspects, the input 191 may be configured to receive the AAC support information 129 via system bus information received via a system bus of a vehicle including the sound control zone 110, as described hereinafter for example.

[0189] In some exemplary aspects, the input 191 may be configured to receive the AAC support information 129 via CAN bus information received via a controller area network (CAN) bus of a vehicle.

[0190] In some exemplary aspects, the input 191 may be configured to receive the AAC support information 129 via A2B bus information received via an A to B (A2B) bus of a vehicle.

[0191] In some exemplary aspects, the input 191 may be configured to receive the AAC support information 129 via MOST bus information received via a media oriented system transport (MOST) bus of a vehicle.

[0192] In some exemplary aspects, the input 191 may be configured to receive the AAC support information 129 via wireless communication information received via a wireless communication link.

[0193] In some exemplary embodiments, input 191 may be configured to receive AAC support information 129 via Ethernet bus information received via the Ethernet bus of the vehicle.

[0194] In other embodiments, input 191 may be configured to receive AAC information 129 via any other wired link or connection, wireless link or connection, and / or any other communication mechanism, connection, link, bus, and / or interface.

[0195] In some exemplary embodiments, AAC information 129 may include sensor information from one or more sensors, as described hereinafter. For example, information source 120 may include one or more sensors, as described hereinafter.

[0196] In some exemplary embodiments, AAC support information 129 may include sensor information from one or more acoustic sensors, as described hereinafter. For example, information source 120 may include one or more acoustic sensors, as described hereinafter.

[0197] In some exemplary embodiments, information source 120 may include one or more acoustic sensors that may be different from and / or independent of the monitoring sensor at monitoring position 103, noise acoustic sensor 119, and / or residual noise acoustic sensor 121, as described hereinafter.

[0198] In some exemplary embodiments, information source 120 may include one or more acoustic sensors that may be included as part of the monitoring sensor at monitoring position 103, noise acoustic sensor 119, and / or residual noise acoustic sensor 121, and / or that may utilize one or more of their functions, as described hereinafter.

[0199] In some exemplary embodiments, the AAC information 129 may be based in part or in whole on acoustic information from, for example, the noise acoustic sensor 119 and / or the residual noise acoustic sensor 121, as described below.

[0200] In some exemplary embodiments, the information source 120 may include one or more environmental sensors configured to sense one or more parameters and / or attributes of the environment of the sound control zone 110, as described below.

[0201] In some exemplary embodiments, 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.

[0202] In some exemplary embodiments, the AAC information 129 may include sensor information from one or more optical and / or image sensors, as described below. For example, the information source 120 may include one or more optical and / or image sensors, such as a camera, as described below.

[0203] In some exemplary embodiments, the AAC information 129 may include any other sensor information from any other additional or alternative sensor.

[0204] In some exemplary embodiments, the information source 120 may include AAC information 129 corresponding to the state of one or more elements and / or settings that affect the AAC configuration, as described below.

[0205] In some exemplary embodiments, the AAC information 129 may include vehicle system configuration information corresponding to the configuration of the operation of one or more vehicle systems of a vehicle including the sound control zone 110, as described below.

[0206] In some exemplary embodiments, the AAC information 129 may include vehicle system configuration information from one or more vehicle systems of the vehicle, for example, as described hereinafter. For example, the information source 120 may include one or more vehicle systems of the vehicle and / or a system controller of the vehicle, for example, as described hereinafter.

[0207] In some exemplary embodiments, the AAC information 129 may include vehicle type information corresponding to the type of the vehicle, for example, as described hereinafter.

[0208] For example, the AAC information 129 may include vehicle type information representing a vehicle type, such as a sports utility vehicle (SUV), hatchback, crossover, convertible, sedan, coupe, sports, minivan, van, station wagon, pickup truck, and the like.

[0209] For example, the AAC information 129 may include vehicle type information representing a vehicle size type, such as mini, medium, large, wide, medium width, narrow width, high, medium high, low, and the like.

[0210] For example, the AAC information 129 may include vehicle type information representing the vehicle manufacturer of the vehicle.

[0211] In some exemplary embodiments, the AAC information 129 may include vehicle sensor information that can be received from one or more sensors of the vehicle system of the vehicle, for example, as described hereinafter.

[0212] In some exemplary embodiments, the AAC information 129 may include vehicle speed information corresponding to the speed of the vehicle, for example, as described hereinafter.

[0213] In some exemplary embodiments, the AAC information 129 may include engine information corresponding to the engine of the vehicle, for example, as described hereinafter.

[0214] For example, the AAC information 129 may include revolutions per minute (RPM), which corresponds to the RPM of the vehicle's engine, eg, as described below.

[0215] In some demonstrative embodiments, AAC information 129 may include brake system information corresponding to the vehicle's brake system, for example, as described below.

[0216] For example, AAC information 129 may include brake system information to indicate the operating status of a main brake system, an emergency brake system, and / or an anti-lock brake system (ABS), and / or any other brake systems, e.g., as described below.

[0217] In some illustrative embodiments, AAC information 129 may include road detection information corresponding to a vehicle's road detection system, for example, as described below.

[0218] For example, the AAC information 129 may include road detection information indicating road types, such as smooth roads, bumpy roads, undulating roads, highways, paved roads, unpaved roads, gravel roads, etc., as described below.

[0219] In some demonstrative embodiments, AAC information 129 may include steering information corresponding to a vehicle's steering system, for example, as described below.

[0220] For example, the AAC information 129 may include steering wheel information indicating the angle of the steering wheel of the vehicle, for example as described below.

[0221] In some illustrative embodiments, the AAC information 129 may include tire information corresponding to the vehicle's tire system, for example, as described below.

[0222] For example, as will be described later, 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.

[0223] In some exemplary embodiments, the AAC information 129 may include seat information corresponding to one or more seats in the vehicle, as will be described later.

[0224] For example, as will be described later, the AAC information 129 may include seat position information corresponding to the arrangement of the driver's seat and / or the arrangement of one or more passenger seats in the vehicle.

[0225] For example, as will be described later, the AAC information 129 may include seat occupancy information corresponding to the occupancy of one or more seats in the vehicle. 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, and the like.

[0226] In some exemplary embodiments, the AAC information 129 may include passenger information corresponding to one or more passengers in the vehicle, as will be described later.

[0227] For example, as will be described later, the AAC information 129 may include passenger information indicating the number, position, location, size, and / or measurements of one or more passengers in the vehicle.

[0228] In some exemplary embodiments, the AAC information 129 may include opening state information corresponding to one or more openings of the vehicle, as will be described later.

[0229] In some exemplary embodiments, the AAC information 129 may include window / roof information corresponding to the windows, doors, trunks, and / or roof of the vehicle, as will be described later.

[0230] For example, as described later, the AAC information 129 may include window information indicating the fully open position, partially open position, number of open windows (e.g., window opening percentage), or closed position of one or more windows of a vehicle, door information indicating open or closed doors, and / or roof type, such as a metal roof or a panoramic roof, roof position, such as an open position, partially open position, number of open roofs (e.g., roof opening percentage), or roof closed position indicating roof information.

[0231] In some exemplary embodiments, the AAC information 129 may include audio system information corresponding to an audio system of a vehicle, as described later, for example.

[0232] For example, as described later, the AAC information 129 may include audio system information indicating one or more audio parameters of the operation of an audio system, such as audio level, audio input, equalizer settings, music level, etc.

[0233] In some exemplary embodiments, the AAC information 129 may include climate information corresponding to the climate inside and / or outside the vehicle, as described later, for example.

[0234] For example, as described later, the AAC information 129 may include temperature information corresponding to the temperature inside and / or outside the vehicle.

[0235] For example, as described later, the AAC information 129 may include humidity information corresponding to the humidity inside and / or outside the vehicle.

[0236] For example, as described later, the AAC information 129 may include precipitation information corresponding to the situation of rain, snow, and / or ice outside the vehicle.

[0237] In some exemplary aspects, the AAC information 129 may include any other additional or alternative information.

[0238] In some exemplary aspects, the controller 193 may be configured to determine a sound control pattern for controlling the sound within the sound control zone 110 based on, for example, the AAC information 129, the plurality of noise inputs 104, and / or the plurality of residual noise inputs 106, as described hereinafter.

[0239] In some exemplary aspects, the AAC controller 102 may include an output 197 for outputting a sound control pattern for a plurality of acoustic transducers. For example, the output 197 may be configured to output the sound control pattern in the form of a sound control signal 109 for controlling the acoustic transducer 108, as described hereinafter.

[0240] In one example, the sound control signal 109 may include a plurality of transducer input signals provided to the plurality of acoustic transducers 108 respectively. For example, the transducer input signal for the acoustic transducer 108 may be configured to cause the acoustic transducer 108 to output an acoustic signal according to the sound control pattern. For example, the plurality of transducer input signals may be configured such that the sound control pattern can be generated by a combination of the acoustic signals output by the plurality of acoustic transducers 108.

[0241] In some exemplary aspects, the AAC controller 102 may be configured to determine AAC parameter settings based on the AAC configuration information 129, as described hereinafter, and determine a sound control pattern for the sound control signal 109 by applying the AAC parameter settings to at least one of, for example, the plurality of noise inputs 104 and / or the plurality of residual noise inputs 106.

[0242] In some exemplary aspects, the AAC parameter settings may include, for example as described hereinafter, settings of prediction filters, settings of transfer functions, settings of an extractor (also referred to as an “acoustic pattern extractor”) for extracting a plurality of reference acoustic patterns including non-identical reference acoustic patterns, and / or settings of any other arbitrary parameters that may be used for determining, generating, updating, configuring, and / or setting a sound control pattern for controlling the acoustic transducer 108.

[0243] In some exemplary aspects, the AAC controller 102 may be configured to determine a prediction filter setting of at least one prediction filter based on, for example, the AAC configuration information 129 as described hereinafter, and to determine a sound control pattern based on, for example, the prediction filter setting.

[0244] In some exemplary aspects, the prediction filter setting may include, for example as described hereinafter, a plurality of prediction filter coefficients for configuring the prediction filter.

[0245] In some exemplary aspects, the plurality of prediction filter coefficients may be represented by, for example as described hereinafter, a prediction filter weight vector.

[0246] In some exemplary aspects, the prediction filter setting may include, for example as described hereinafter, a prediction filter weight vector applied by the prediction filter to determine a sound control pattern for the sound control signal 109 based on at least one of the plurality of noise inputs 104 and / or the plurality of residual noise inputs 106.

[0247] In other aspects, the AAC controller 102 may be configured to determine any other arbitrary additional or alternative prediction filter settings based on, for example, the AAC configuration information 129.

[0248] In some exemplary embodiments, the AAC controller 102 may be configured to determine a path transfer function setting of one or more path transfer functions based on, for example, AAC configuration information 129 as described later, and apply the path transfer function setting to determine a sound control pattern for the sound control signal 109 based on at least one of, for example, a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106.

[0249] In some exemplary embodiments, the path transfer function setting may include, for example, setting a path transfer function between the acoustic transducer 108 and an acoustic sensing position as described later.

[0250] In some exemplary embodiments, the path transfer function setting may include, for example, setting a path transfer function between the acoustic transducer 108 and a physical acoustic sensing position of a physical acoustic sensor as described later.

[0251] In some exemplary embodiments, the path transfer function setting may include, for example, setting a path transfer function between the acoustic transducer 108 and a virtual acoustic sensing position of a virtual acoustic sensor as described later.

[0252] In some exemplary embodiments, the path transfer function setting may include, for example, setting a path transfer function between the acoustic transducer 108 and the noise sensing position 105 as described later.

[0253] In some exemplary embodiments, the path transfer function setting may include, for example, setting a path transfer function between the acoustic transducer 108 and the residual noise sensing position 107 as described later.

[0254] In some exemplary embodiments, the path transfer function setting may include setting a path transfer function between the acoustic transducer 108 and the monitoring position 103. For example, at least one of the one or more residual noise inputs 106 may be based on, for example, a monitoring input sensed at the monitoring position 103.

[0255] For example, the AAC controller 102 may be configured to determine a setting of a path transfer function between the acoustic transducer 108 and the monitoring position 103 of the monitoring sensor, which is used to determine the residual noise input 106.

[0256] For example, the AAC controller 102 may be configured to determine a sound control pattern for controlling the sound within the sound control zone 110 based on, for example, a setting of the path transfer function between the acoustic transducer 108 and the monitoring position 103 of the monitoring sensor.

[0257] In one example, the monitoring position 103 of the monitoring sensor used to determine the residual noise input 106 may be, for example, within the sound control zone 110 as described above.

[0258] In one example, the monitoring position 103 of the monitoring sensor used to determine the residual noise input 106 may be, for example, outside the sound control zone 110 as described above.

[0259] In some exemplary aspects, the AAC controller 102 may be configured to determine a path transfer function setting of the path transfer function between the acoustic transducer 108 and the noise monitoring position 105, for example, as described later.

[0260] In some exemplary aspects, the AAC controller 102 may be configured to determine a path transfer function setting of the path transfer function between the acoustic transducer 108 and the residual noise sensing position 107, for example, as described later.

[0261] In some exemplary aspects, the AAC controller 102 may be configured to determine a noise extraction function based on, for example, the AAC configuration information 129, for example, as described later.

[0262] In some exemplary embodiments, the AAC controller 102 may be configured to determine one or more extracted acoustic patterns by applying a noise extraction function to at least one of a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106, as will be described later, and to determine a sound control pattern for the sound control signal 109 based on, for example, the one or more extracted acoustic patterns.

[0263] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control profile based on the AAC configuration information 129 and to determine a sound control pattern based on the sound control profile, as will be described later.

[0264] In some exemplary embodiments, the sound control profile may include settings of one or more sound control parameters, and the AAC controller 102 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the settings of the one or more sound control parameters according to the sound control profile, as will be described later.

[0265] In some exemplary embodiments, the memory 198 may be configured to store a plurality of sound control profiles corresponding to a plurality of sound control configurations, for example, by the controller 193, as will be described later.

[0266] In some exemplary embodiments, the controller 193 may be configured to select and retrieve a sound control profile selected from the plurality of sound control profiles in the memory 198 based on, for example, the AAC configuration information 129, as will be described later.

[0267] In some exemplary embodiments, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the selected sound control profile, as will be described later.

[0268] In some exemplary embodiments, the plurality of sound control profiles may include one or more user-based profiles corresponding to one or more users, for example, as described below.

[0269] In some exemplary embodiments, the user-based profile corresponding to a user may include settings of one or more sound control parameters based on, for example, the user's preferences, as described below.

[0270] For example, the 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 low-level noise reduction, a noise reduction "high" setting that sets high-level noise reduction, and the like.

[0271] For example, the user-based profile corresponding to a user may include settings such as noise reduction targeted for each user, for example, configured, for example, targeted noise reduction for each predetermined user seat position, targeted noise reduction optimized for one or more specific seats, such as front seats or rear seats, noise reduction optimized for one or more specific frequency bands, and the like.

[0272] For example, the user-based profile corresponding to a user may include, for example, audio settings for audio listened to within the sound control zone 110, such as the sound level of the audio, the equalizer settings of the audio, and the like.

[0273] In some exemplary embodiments, the user-based profile may correspond to a user to whom it is permitted to control user preferences for the sound control zone 110, for example, as described below.

[0274] In one example, the user-based profile may correspond to a user of sound control zone 110. For example, the user-based profile of a vehicle driver may include settings of one or more sound control parameters based on the driver's preferences for the sound control zone 110 implemented, for example, with respect to the driver's seat of the vehicle.

[0275] In another example, the user-based profile may correspond to a first user for controlling user preferences for the sound control zone 110, and this may be used by a second user. For example, the user-based profile of a vehicle driver may include settings of one or more sound control parameters based on the driver's preferences for the sound control zone 110 implemented, for example, with respect to one or more passenger seats of the vehicle.

[0276] In some exemplary aspects, the AAC configuration information 129 may include, for example, user identity information corresponding to the user's identity. For example, the controller 193 may be configured to select and retrieve a sound control profile selected from a plurality of sound control profiles in the memory 198 based on, for example, the user identity information in the AAC configuration information 129.

[0277] In some exemplary aspects, the AAC controller 102 may be configured to selectively mute the sound control pattern for the sound control signal 109 based on, for example, the AAC configuration information 129, as described hereinafter.

[0278] In some exemplary aspects, the AAC controller 102 may be configured to adjust the level of the sound control pattern for the sound control signal 109 based on, for example, the AAC configuration information 129, as described hereinafter.

[0279] In some exemplary embodiments, the AAC controller 102 may be configured to determine the setting of at least one AAC parameter based on, for example, the AAC information 129 as described later, and determine a sound control pattern for the sound control signal 109 based on, for example, the AAC parameter setting.

[0280] In some exemplary embodiments, the AAC parameter setting may include, for example, as described later, the setting of a prediction filter, the setting of a transfer function, the setting of an adaptive AAC parameter, a plurality of reference acoustic patterns, for example, the setting of an extractor (also referred to as an "acoustic pattern extractor") for extracting non-identical reference acoustic patterns, and / or the setting of any other arbitrary parameters that may be used for determining, generating, updating, configuring, and / or adapting a sound control pattern for controlling the acoustic transducer 108.

[0281] In some exemplary embodiments, the AAC controller 102 may be configured to determine the prediction filter setting of at least one prediction filter based on the AAC information 129 as described later, and determine a sound control pattern for the sound control signal 109 based on, for example, the prediction filter setting.

[0282] In some exemplary embodiments, the prediction filter setting may include, for example, as described later, a plurality of prediction filter coefficients and / or a prediction filter weight vector applied by a prediction filter to determine a sound control pattern based on, for example, a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106.

[0283] In some exemplary embodiments, the AAC controller 102 may be configured to determine the transfer function setting of one or more transfer functions based on the AAC information 129 as described later, and apply the transfer function setting to determine a sound control pattern for the sound control signal 109 based on, for example, a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106.

[0284] In some exemplary embodiments, the AAC controller 102 may be configured to determine a path transfer function setting of a path transfer function between the acoustic transducer 108 and the noise sensing location 105, for example, based on the AAC information 129 as described later.

[0285] In some exemplary embodiments, the AAC controller 102 may be configured to determine a path transfer function setting of a path transfer function between the acoustic transducer 108 and the residual noise sensing location 107, for example, based on the AAC information 129 as described later.

[0286] In some exemplary embodiments, the AAC controller 102 may be configured to determine a path transfer function setting of a path transfer function between the acoustic transducer 108 and the monitoring location 103, for example, based on the AAC information 129 as described later.

[0287] In some exemplary embodiments, the AAC controller 102 may be configured to extract a plurality of reference acoustic patterns including a plurality of non-identical reference acoustic patterns that are, for example, statistically independent, from the plurality of noise inputs 104.

[0288] In some exemplary embodiments, the AAC controller 102 may be configured to extract a plurality of residual noise acoustic patterns including a plurality of non-identical residual noise acoustic patterns that are, for example, statistically independent, from the plurality of residual noise inputs 106.

[0289] For example, the controller 193 may include an extractor (also referred to as an "acoustic pattern extractor" or "feature extractor") for extracting a plurality of reference acoustic patterns and / or a plurality of residual noise acoustic patterns.

[0290] As used herein, the phrase "non-identical acoustic patterns" may refer to multiple acoustic patterns that are independent with respect to at least one characteristic and / or attribute, such as energy, amplitude, phase, frequency, direction, one or more statistical signal characteristics, and the like.

[0291] In some exemplary embodiments, the controller 193 may extract a plurality of reference acoustic patterns by applying a predetermined reference noise extraction function to a plurality of reference noise inputs 104.

[0292] In some exemplary embodiments, the extraction of a plurality of acoustic patterns may be used to model the primary pattern of the input 104, for example, as a combination of a predetermined number of non-identical acoustic patterns, each corresponding to a non-identical modeled acoustic source, for example.

[0293] In one example, it may be assumed that one or more predicted noise patterns that are expected to affect the sound control zone 110 may be generated by one or more of road noise, wind noise, engine noise, and the like. Accordingly, the controller 193 may be configured to select one or more reference acoustic patterns based on one or more attributes of a road noise pattern, a wind noise pattern, an engine noise pattern, and / or any other noise pattern.

[0294] In some exemplary embodiments, the controller 193 may extract a plurality of residual noise acoustic patterns by applying a predetermined residual noise extraction function to a plurality of residual noise inputs 106.

[0295] In some exemplary embodiments, the AAC controller 102 may determine the acoustic pattern extractor settings of the acoustic pattern extractor based on the AAC information 129, for example, as described hereinafter, and may be configured to determine a sound control pattern for the sound control signal 109 based on the acoustic pattern extractor settings, for example.

[0296] In some exemplary embodiments, the acoustic pattern extractor settings may include one or more acoustic pattern extractor coefficients that are applied by the acoustic pattern extractor to determine, for example as described below, a plurality of reference acoustic patterns and / or a plurality of residual noise acoustic patterns.

[0297] In some exemplary embodiments, the controller 193 is configured to determine, update, and / or adjust, for example in real time, the setting of at least one acoustic pattern extractor parameter based on the AAC information 129, for example as described below, and to determine a sound control pattern for the sound control signal 109 based on, for example, the acoustic pattern extractor parameter setting.

[0298] In some exemplary embodiments, the acoustic pattern extractor parameter setting may include the setting of one or more coefficients, one or more weight parameters, and / or any other optional parameters that can be utilized by the acoustic pattern extractor when extracting a plurality of non-identical reference acoustic patterns and / or a plurality of non-identical residual noise acoustic patterns.

[0299] In some exemplary embodiments, the AAC information 129 may include passenger tracking information indicating the position of the passenger's head and / or ears.

[0300] For example, the information source 120 may include a camera, an image sensor, an optical sensor, and / or any other optional sensor 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 prediction filter settings, path transfer function settings, and / or acoustic pattern extractor settings, based on, for example, the passenger tracking information.

[0301] In one example, the AAC controller 102 can be configured to set one or more AAC parameters, such as prediction filter settings, transfer function settings, and / or acoustic pattern extractor settings, for example in real time, based on changes in the position of the head and / or ears of a passenger within the sound control zone 110, for example in real time.

[0302] In one example, the AAC controller 102 can be configured to set, for example in real time, the transfer function settings of the transfer function between the acoustic transducer 108 and one or more residual noise sensing positions 107, the transfer function settings of the transfer function between the acoustic transducer 108 and one or more noise sensing positions 105, and / or the transfer function settings of the transfer function between the acoustic transducer 108 and one or more monitoring positions 103, based on changes in the position of the head and / or ears of a passenger within the sound control zone 110, for example in real time.

[0303] In some exemplary aspects, the AAC information 129 may include seat position information corresponding to the arrangement of one or more seats within the vehicle. For example, the AAC information 129 may include seat position information corresponding to the arrangement of the driver's seat and / or the arrangement of one or more passenger seats within the vehicle.

[0304] In one example, the AAC controller 102 can be configured to set one or more AAC parameters, such as prediction filter settings, transfer function settings, and / or acoustic pattern extractor settings, for example in real time, based on, for example, seat position information.

[0305] In one example, the AAC controller 102 may be configured to set, for example in real time, the path transfer function between the acoustic transducer 108 and one or more residual noise sensing positions 107, the path transfer function between the acoustic transducer 108 and one or more noise sensing positions 105, and / or the path transfer function between the acoustic transducer 108 and one or more monitoring positions 103, for example based on changes in the seat positions of the driver and / or passengers, such as in real time.

[0306] In some exemplary embodiments, the AAC information 129 may include passenger information corresponding to one or more passengers in the vehicle. For example, the AAC information 129 may include passenger information indicating the number, location, position, size, and / or measurements of one or more passengers in the vehicle.

[0307] In one example, the AAC controller 102 may be configured to set, for example in real time, one or more AAC parameters, such as prediction filter settings, path transfer function settings, and / or acoustic pattern extractor settings, for example based on passenger information.

[0308] In one example, the AAC controller 102 may be configured to set, for example in real time, the path transfer function between the acoustic transducer 108 and one or more residual noise sensing positions 107, the path transfer function between the acoustic transducer 108 and one or more noise sensing positions 105, the path transfer function between the acoustic transducer 108 and one or more monitoring positions 103, the acoustic pattern extractor settings, and / or the prediction filter settings, for example based on the number, location, position, size, and / or measurements of one or more passengers in the vehicle, such as in real time.

[0309] In some exemplary embodiments, the AAC information 129 may include climate information corresponding to the climate in the vehicle.

[0310] In one example, the AAC controller 102 can be configured to set one or more AAC parameters, such as prediction filter settings, path transfer function settings, and / or acoustic pattern extractor settings, for example in real time, based on changes in the climate inside the vehicle, for example in real time.

[0311] In one example, the AAC controller 102 can be configured to set, for example in real time, the path transfer function settings of the path transfer function between the acoustic transducer 108 and one or more residual noise sensing positions 107, the path transfer function settings of the path transfer function between the acoustic transducer 108 and one or more noise sensing positions 105, the path transfer function settings of the path transfer function between the acoustic transducer 108 and one or more monitoring positions 103, the acoustic pattern extractor settings, and / or the prediction filter settings, based on changes in the climate inside the vehicle, for example in real time. For example, the AAC controller 102 can be configured to set, for example in real time, the path transfer function settings of the path transfer function between the acoustic transducer 108 and one or more residual noise sensing positions 107, the path transfer function settings of the path transfer function between the acoustic transducer 108 and one or more noise sensing positions 105, the path transfer function settings of the path transfer function between the acoustic transducer 108 and one or more monitoring positions 103, the acoustic pattern extractor settings, and / or the prediction filter settings based on the detected changes in the temperature and / or humidity levels inside the vehicle indicated by, for example, the AAC information 129.

[0312] In some exemplary embodiments, the AAC information 129 can include vehicle system information corresponding to the noise generating vehicle system of the vehicle, and the AAC controller 102 can be configured to determine a sound control pattern for the sound control signal 109, for example as described hereinafter, based on the vehicle system information, for example.

[0313] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control pattern for the sound control signal 109, for example, based on vehicle system information such that the sound control pattern reduces or eliminates noise from a noise generating vehicle system within the sound control zone 110, as described further below.

[0314] In some exemplary embodiments, the noise generating vehicle system may include, for example, a vehicle engine, vehicle tires, a vehicle braking system, a vehicle steering system, a vehicle air conditioning system, and / or any other optional system of the vehicle.

[0315] In some exemplary embodiments, the AAC information 129 may include, for example, vehicle system setting information representing the settings of the vehicle systems of the vehicle, as described further below.

[0316] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control pattern based on the vehicle system setting information, as described further below.

[0317] In some exemplary embodiments, the AAC controller 102 may be configured to determine a first sound control pattern for the sound control signal 109 based on the AAC information 129 including first vehicle system setting information representing a first setting of the vehicle system, as described further below.

[0318] In some exemplary embodiments, the AAC controller 102 may be configured to determine a second sound control pattern, different from the first sound control pattern, for a second control signal 109 based on the 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, as described further below.

[0319] In some exemplary embodiments, the AAC controller 102 may be configured to determine and / or set a sound control pattern for the sound control signal 109, for example, based on a change in vehicle system setting information representing a change in the setting of the vehicle system, as described later.

[0320] In some exemplary embodiments, the AAC information 129 may include operation mode information representing the operation mode of the vehicle system of the vehicle, as described later, for example.

[0321] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control pattern for the sound control signal 109, for example, based on the operation mode information, as described later.

[0322] In some exemplary embodiments, the AAC controller 102 may be configured to determine a first sound control pattern for the sound control signal 109, for example, based on the AAC information 129 including first operation mode information representing the first operation mode of the vehicle system, as described later.

[0323] In some exemplary embodiments, the AAC controller 102 may be configured to determine a second sound control pattern for the sound control signal 109, which is different from the first sound control pattern, for example, based on the AAC information 129 including second operation mode information representing the second operation mode of the vehicle system different from the first operation mode of the vehicle system, as described later.

[0324] In some exemplary embodiments, the AAC controller 102 may be configured to set a sound control pattern for the sound control signal 109, for example, based on a change in operation mode information representing a change in the operation mode of the vehicle system, as described later.

[0325] In some exemplary embodiments, the AAC controller 102 may be configured to determine a sound control profile based on, for example, the AAC information 129 as described later, and to determine a sound control pattern for the sound control signal 109 based on, for example, the sound control profile.

[0326] In some exemplary embodiments, the sound control profile may include settings of one or more sound control parameters, and the AAC controller 102 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the settings of one or more sound control parameters as described later.

[0327] In some exemplary embodiments, the memory 198 may be configured to store a plurality of sound control profiles (AAC profiles) 199 each corresponding to a plurality of sound control configurations as described later.

[0328] In some exemplary embodiments, the AAC profile 199 corresponding to a specific sound control configuration may include, for example, settings of one or more AAC parameters corresponding to the specific sound control configuration, such as prediction filter settings, transfer function settings, and / or acoustic pattern extractor settings as described later.

[0329] In some exemplary embodiments, the AAC controller 102 may be configured to select a sound control profile selected from a plurality of sound control profiles 198 based on, for example, the AAC information 129 as described later, and to determine a sound control pattern based on the selected sound control profile.

[0330] In some exemplary aspects, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on the AAC information 129 such that, for example as described hereinafter, the sound control pattern controls, reshapes, reduces, or removes noise from one or more noise sources in at least one sound control zone 110.

[0331] In one example, the AAC information 129 may include RPM information of the vehicle's engine.

[0332] In one example, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the RPM information such that the sound control pattern reduces or removes noise from the engine and / or modify the sound control pattern to improve the reduction of other noise sources within at least one sound control zone 110.

[0333] In another example, the controller 193 may be configured to determine and / or modify a sound control pattern for the sound control signal 109 based on RPM information based on one or more other arbitrary additional or alternative criteria, for example, to support the control and / or reduction of one or more other sound parameters and / or to support the reduction and / or removal of noise from one or more other noise sources.

[0334] 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 based on, for example, the AAC information 129 as described hereinafter.

[0335] 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 based on, for example, RPM information and / or other any type of information within the AAC information 129, as will be described later.

[0336] In another example, the AAC information 129 may include window / roof information indicating 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 pattern for the sound control signal 109 based on, for example, the window / roof information, such that the sound control pattern reduces or eliminates external noise from the vehicle's environment, such as wind noise, road noise, etc., in at least one sound control zone 110.

[0337] In another example, the AAC information 129 may include road detection information corresponding to the vehicle's road detection system. For example, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the road detection information, such that the sound control pattern reduces or eliminates external noise from the vehicle's environment in at least one sound control zone 110 based on, for example, the road type indicated by the road detection information.

[0338] In another example, the AAC information 129 may include tire information corresponding to the vehicle's tire system. For example, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the tire information, such that the sound control pattern reduces or eliminates noise from the tires in at least one sound control zone 110 based on, for example, 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.

[0339] In 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 pattern for the sound control signal 109 based on, for example, the climate information. For example, the sound control pattern may reduce or remove external noise from the vehicle's environment, such as rain noise, wind noise, road noise, and / or any other noise, in at least one sound control zone 110.

[0340] In another example, the AAC information 129 may include steering information corresponding to the vehicle's steering system. For example, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the steering information. For example, the sound control pattern may reduce or remove external noise from the vehicle's environment in at least one sound control zone 110 based on, for example, the angle of the vehicle's steering wheel, such as the left / right steering angle.

[0341] In another example, the AAC information 129 may include brake system information indicating the operating state of the vehicle's main brake system, emergency brake system, antilock brake system (ABS), and / or any other brake system. For example, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on, for example, the brake system information. For example, the sound control pattern may reduce or remove external noise from the vehicle's environment in at least one sound control zone 110 based on, for example, the operating state of the brake system.

[0342] In some exemplary embodiments, the AAC controller 193 may be configured to determine and / or set, for example, in real time, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on, for example, the AAC information 129, as described hereinafter.

[0343] In some exemplary embodiments, the AAC controller 193 may be configured to determine and / or set, for example in real time, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, for example by selectively generating the sound control signal 109 and / or selectively providing the sound control signal 109 to the acoustic transducer 108, as will be described later.

[0344] In some exemplary embodiments, the AAC controller 193 may be configured to determine and / or set, for example in real time, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, for example by selecting whether to provide the sound control signal 109 to the acoustic transducer 108, as will be described later.

[0345] In some exemplary embodiments, the AAC controller 193 may be configured to, for example as will be described later, for example based on the AAC information 129, mute, for example dynamically and / or selectively, in real time, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, and / or reduce, for example dynamically and / or selectively in real time, the level of a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109.

[0346] In some exemplary embodiments, the AAC controller 193 may be configured to, for example as will be described later, based on the AAC information 129, dynamically identify in real time, for example, one or more predetermined situations ("mute situations") in which the sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 is muted or set to a reduced level.

[0347] In some exemplary embodiments, the AAC controller 193 may be configured to mute or reduce the level of a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on, for example, a predetermined mute situation, as will be described later.

[0348] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, by, for example, setting a prediction filter (PF) to zero, as will be described later.

[0349] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, by, for example, setting the input from the reference sensor 104 to zero, as will be described later.

[0350] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, by, for example, setting the sound control signal 109 to zero, as will be described later.

[0351] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, by, for example, selecting not to invoke the AAC function for generating the sound control pattern, as will be described later.

[0352] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, for example, by selectively setting some or all of the input and / or output of, for example, the acoustic pattern extractor to zero, as will be described later.

[0353] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on, for example, any other additional or alternative settings and / or mechanisms.

[0354] In some exemplary embodiments, the AAC information 129 may include voice detection information indicating the detected voice of one or more passengers in the vehicle.

[0355] In some exemplary embodiments, the information source 120 may include a voice detector for generating voice detection information.

[0356] In one example, the voice detector may be configured to generate voice detection information based on, for example, acoustic information from the reference acoustic sensor 104 and / or the monitoring acoustic sensor.

[0357] In another example, the voice detector may be configured to generate voice detection information based on, for example, acoustic information from one or more other acoustic sensors, such as dedicated voice detection sensors and / or any other dedicated or non-dedicated sensors.

[0358] In some exemplary embodiments, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on, for example, identifying that the AAC information 129 indicates the detection of voice.

[0359] In some exemplary aspects, the AAC information 129 may include audio information corresponding to audio audible within the vehicle.

[0360] In some exemplary aspects, the information source 120 may include an audio source or an audio controller for providing and / or controlling audio audible within the vehicle.

[0361] In some exemplary aspects, the AAC controller 193 may be configured to selectively set one or more AAC parameters for generating the sound control signal 109, for example, based on audio information.

[0362] In some exemplary aspects, the AAC controller 193 may be configured to selectively set one or more AAC parameters for generating the sound control signal 109, for example, based on the audio level and / or the equalization level of the audio audible within the vehicle.

[0363] In some exemplary aspects, the AAC controller 193 may be configured to mute, for example, the sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, based on, for example, the level of the output of the acoustic transducer 108. For example, the AAC controller 193 may be configured to mute the sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on, for example, the detection that the output level of the acoustic transducer 108 is greater than a predetermined threshold (the "maximum speaker threshold") and / or based on the detection that the output level of the acoustic transducer 108 is less than a predetermined threshold (the "minimum speaker threshold").

[0364] In some exemplary embodiments, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on, for example, the level of the noise input 104. For example, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on the detection that, for example, the level of the noise input 104 is greater than a predetermined threshold (a "maximum reference threshold"), and / or based on the detection that the level of the noise input 104 is less than a predetermined threshold (a "minimum reference threshold").

[0365] In some exemplary embodiments, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on, for example, the residual noise input 106. For example, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on the detection that, for example, the level of the residual noise input 106 is greater than a predetermined threshold (a "maximum residual threshold"), and / or based on the detection that the level of the residual noise input 106 is less than a predetermined threshold (a "minimum residual threshold").

[0366] In some exemplary embodiments, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on, for example, the level of a monitoring input corresponding to the monitoring location 103. For example, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via, for example, the sound control signal 109, based on the detection that, for example, the level of the monitoring input is greater than a predetermined threshold (a "maximum monitoring threshold"), and / or based on the detection that the level of the monitoring input is less than a predetermined threshold (a "minimum monitoring threshold").

[0367] In some exemplary embodiments, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, based on a determination that, for example, one or more acoustic sensors are malfunctioning and / or operating incorrectly.

[0368] In some exemplary embodiments, the AAC controller 193 may be configured to detect that one or more acoustic sensors are malfunctioning and / or operating incorrectly, for example, based on the AAC information 129.

[0369] In some exemplary embodiments, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, based on a determination that, for example, one or more reference acoustic sensors 119 are malfunctioning and / or operating incorrectly.

[0370] In some exemplary embodiments, the AAC controller 193 may be configured to detect one or more reference acoustic sensors 119 that are malfunctioning and / or operating incorrectly, for example, based on the noise input 104 and / or any other information within the AAC information 129.

[0371] In some exemplary embodiments, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109, based on a determination that, for example, one or more residual noise acoustic sensors 121 are malfunctioning and / or operating incorrectly.

[0372] In some exemplary embodiments, the AAC controller 193 may be configured to detect one or more residual noise acoustic sensors 121 that are malfunctioning and / or operating incorrectly, for example, based on the residual noise input 106 and / or any other information within the AAC information 129.

[0373] In some exemplary aspects, 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 speed information corresponding to the speed of a vehicle including, for example, the sound control zone 110.

[0374] 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 a detection indicating that the speed information, for example, indicates that the speed of the vehicle exceeds a predetermined vehicle speed and / or is outside a predetermined vehicle speed range.

[0375] In some exemplary aspects, 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 opening state information corresponding to one or more openings of a vehicle including, for example, the sound control zone 110.

[0376] 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 a detection indicating that the opening state information, for example, indicates that a door of the vehicle is open, a window is open by more than a predetermined opening percentage, a trunk of the vehicle is open, and / or a roof of the vehicle is open by more than a predetermined opening percentage.

[0377] In some exemplary aspects, 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 tire information corresponding to a tire system of a vehicle including, for example, the sound control zone 110.

[0378] In one example, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on a detection indicating that, for example, the tire information indicates that the tire pressure of one or more tires is not within a predetermined tire pressure range.

[0379] In some exemplary aspects, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on climate information corresponding to the climate inside the vehicle including, for example, the sound control zone 110.

[0380] In one example, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on a detection indicating that, for example, the climate information indicates that the temperature inside the vehicle is not within a predetermined temperature range and / or the humidity level inside the vehicle is not within a predetermined humidity level range.

[0381] In some exemplary aspects, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on climate information corresponding to the climate outside the vehicle including, for example, the sound control zone 110.

[0382] In one example, the AAC controller 193 may be configured to mute, for example, a sound control pattern provided to the acoustic transducer 108 via the sound control signal 109 based on a detection indicating that, for example, the climate information indicates that the temperature outside the vehicle is not within a predetermined temperature range and / or the humidity level outside the vehicle is not within a predetermined humidity level range.

[0383] In some exemplary aspects, 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 vehicle system information corresponding to a vehicle system of a vehicle including, for example, the sound control zone 110.

[0384] 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 a detection indicating that the vehicle system information, for example, indicates that the operating conditions of the vehicle system are not within a predetermined operating condition range.

[0385] 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 a detection indicating that the vehicle system information, for example, indicates that the engine RPM is not within a predetermined RPM range.

[0386] 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 a detection indicating that the vehicle system information, for example, indicates that the operating conditions of the vehicle's air conditioning system are not within a predetermined operating condition range and / or the blower speed of the vehicle's air conditioning system is not within a predetermined blower operating range.

[0387] In some exemplary aspects, the controller 193 may be configured to set a sound control pattern for the sound control signal 109 based on a change in the AAC information 129, which represents a change in the acoustic composition of the operation of the AAC system, for example as described hereinafter.

[0388] For example, the controller 193 may be configured to monitor, for example, dynamically and / or continuously monitor the AAC input 129 in order to detect changes in the AAC information 129, for example in real time.

[0389] For example, the controller 193 may be configured to update, for example, in real time and / or continuously, the sound control pattern for the sound control signal 109 based on, for example, the detected change in the AAC information 129.

[0390] In some exemplary aspects, the AAC system 100 may include, for example, as described hereinafter, a neural network-based (NN-based) AAC system.

[0391] In some exemplary aspects, the NN-based AAC system may utilize one or more NNs to estimate and / or determine, for example, as described hereinafter, one or more parameters, outputs, functions, etc. that may be used for the determination, setting, and / or generation of the sound control signal 109.

[0392] In some exemplary aspects, the AAC controller 102 may include an NN 150 that is trained to generate an NN output 159 based on, for example, as described hereinafter, an NN input that includes the AAC configuration information 129.

[0393] In some exemplary aspects, the NN 150 may include, for example, as described hereinafter, a deep neural network (DNN). In other aspects, any other NN type, architecture, and / or topology may be used.

[0394] In some exemplary aspects, the AAC controller 102 may be configured to generate the sound control signal 109 based on, for example, as described hereinafter, the NN output 159 of the NN 150.

[0395] In some exemplary aspects, the NN 150 may be trained to generate, for example, as described hereinafter, a sound control pattern for controlling the sound within the sound control zone 110 based on, for example, the AAC information 129.

[0396] In some exemplary embodiments, the NN150 can be trained to generate a sound control pattern for controlling the sound within the sound control zone 110, for example, based on the AAC information 129 and / or the plurality of noise inputs 104, as described hereinafter.

[0397] In some exemplary embodiments, the NN150 can be trained to generate an NN output 159 based on an NN input that includes, for example, the AAC configuration information 129 and the plurality of noise inputs 104, as described hereinafter.

[0398] In some exemplary embodiments, the NN150 can be trained to generate a sound control pattern for controlling the sound within the sound control zone 110, for example, based on, for example, the AAC information 129, the plurality of noise inputs 104, and optionally the plurality of residual noise inputs 106, as described hereinafter.

[0399] In some exemplary embodiments, the NN150 can be trained to generate a sound control pattern for controlling the sound within the sound control zone 110, for example, based on, for example, the acoustic information corresponding to a plurality of acoustic sensing positions, as described hereinafter.

[0400] In some exemplary embodiments, the plurality of acoustic sensing positions can include, for example, the one or more physical sensing positions of the one or more physical acoustic sensors, as described hereinafter.

[0401] In some exemplary embodiments, the plurality of acoustic sensing positions can include, for example, the one or more virtual sensing positions of the one or more virtual acoustic sensors, as described hereinafter.

[0402] In some exemplary embodiments, the plurality of acoustic sensing positions can include, for example, the one or more noise sensing positions, as described hereinafter.

[0403] In some exemplary embodiments, the plurality of acoustic sensing positions may include one or more residual noise sensing positions, for example as described below.

[0404] In some exemplary embodiments, the plurality of acoustic sensing positions may include one or more monitoring sensing positions, for example as described below.

[0405] In some exemplary embodiments, the AAC controller 102 may be configured to apply canceling noise pre-calibrated to the sound control pattern generated by the NN150. For example, the pre-calibrated canceling noise may be based on, for example, filter calibration for reducing noise within the noise control zone 110 (FIG. 2), for example, optimal filter calibration.

[0406] In some exemplary embodiments, the NN150 may be trained to generate a sound control pattern for controlling the sound within the sound control zone 110 based on, for example, as described below, for example, AAC information 129, for example, a plurality of noise inputs 104 corresponding to the noise sensing position 105 and / or the monitoring position 103, and / or for example, a plurality of residual noise inputs corresponding to the residual noise sensing position 105.

[0407] In other embodiments, the NN150 may be trained to generate an NN output 159 including settings of one or more sound control parameters that can be used to generate a sound control pattern for controlling the sound within the sound control zone 110, for example as described below.

[0408] In some exemplary embodiments, the NN150 may be trained to generate an NN output 159 including settings of one or more PF parameters and / or transducer transfer function (TTF) parameters, for example speaker transfer function (STF) parameters, based on, for example, as described below, for example, AAC information 129.

[0409] In this specification, some exemplary aspects are described with respect to, for example, the STF and / or STF parameters corresponding to a speaker. These aspects may be implemented with respect to, for example, any other TTF and / or TTF parameters corresponding to any other transducer.

[0410] In some exemplary aspects, the controller 193 may be configured to generate the sound control signal 109 based on, for example, one or more PF parameters and / or TTF parameters, such as STF parameters, as described hereinafter.

[0411] In some exemplary aspects, an NN-based AAC system, such as the AAC system 100, may be implemented to provide a technical solution for improving ANC performance under various conditions, for example, while supporting the use of a set of reduced, for example, minimal measured transfer functions (TFs), as described hereinafter.

[0412] In some exemplary aspects, an NN-based AAC system, such as the AAC system 100, may be implemented to provide a technical solution that enables rapid adaptation of a new ANC configuration, for example, by utilizing an existing database.

[0413] In some exemplary aspects, an NN-based AAC system, such as the AAC system 100, may be implemented to provide a technical solution for reducing, for example, minimizing the number of necessary calibration tests for the NN-based AAC system by extrapolating an estimated transfer function from a minimal set of measured TFs.

[0414] In some exemplary aspects, an NN-based AAC system, such as the AAC system 100, may be implemented to provide a technical solution suitable for implementation with respect to a general vehicle cabin configuration, such as a seat implementation, and / or to enable improvement of the NN-based AAC system by, for example, certain training.

[0415] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, can be implemented to provide a technical solution that utilizes an activation function to extract performance from the non-linear portion of the TF, as compared to other AAC systems that can only be performed on the linear portion of the TF, for example, between reference microphone 119 and error microphone 121.

[0416]

[0417] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, may provide a technical solution that supports robustness for dynamic scenarios. For example, the NN-based AAC system can be trained by multiple scenarios, for example, without increasing the size and / or complexity of the NN-based AAC system from the perspective of memory and / or space, as compared to other AAC systems that may require different PFs for each of different scenarios, such as different road types, temperatures, seat positions, etc.

[0418] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, can be implemented to provide a technical solution that utilizes some parameters that may be linearly related to some input features and / or parameters in, for example, AAC information 129. An AAC system that implements this linear dependency may be advantageous as compared to other AAC systems that may have a non-linear dependency, such as an exponential function dependency, on some of its input features and / or parameters.

[0419] ​​​​In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, may be implemented to provide a technical solution configured to conform to, for example, the dynamic MTF and / or dynamic TTF of the AAC system, such as STF, as described later.

[0420] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, may be implemented to provide a technical solution based on a neural network architecture that can span an acoustic space having, for example, input parameters and / or measurement parameters as axes, based on, for example, AAC information 129.

[0421] In one example, NN150 may be configured to utilize, for example, as described later, an NN input including AAC information 129, an NN outer layer including the impulse response of a noise source, and / or an NN target function including, for example, the mean squared error of an error microphone within target zone 110.

[0422] In other embodiments, any other NN architecture, NN outer layer, and / or NN target function may be utilized.

[0423] In some exemplary embodiments, NN150 may be trained in a training / learning phase, for example, as described later, to determine, for example, sound control signal 109.

[0424] In some exemplary embodiments, NN150 may be trained in a training / learning phase to determine one or more parameters and / or functions that can be utilized, for example, as described later, to determine sound control signal 109.

[0425] In some exemplary embodiments, NN150 may be trained in a training / learning phase to determine one or more parameters of the PF that can be utilized, for example, as described later, in sound control signal 109.

[0426] In some exemplary embodiments, NN150 can be trained in a training / learning phase to determine, for example, one or more parameters of a transducer, such as the TTF of transducer 108, such as the STF, as described hereinafter.

[0427] In some exemplary embodiments, NN150 can be trained in a training / learning phase to determine, for example, the microphone transfer function of an error microphone, such as error sensor 121, as described hereinafter.

[0428] In some exemplary embodiments, NN150 can be trained in a training / learning phase to determine, for example, the microphone transfer function of a monitoring microphone at monitoring location 103, as described hereinafter.

[0429] In some exemplary embodiments, NN150 can be trained in a training / learning phase to determine any other additional or alternative parameters, functions, and / or settings that can be used, for example, to determine, for example, sound control signal 109, as described hereinafter.

[0430] In some exemplary embodiments, NN150 can be trained, for example, as described hereinafter, using, for example, real-time training based on, for example, reinforcement learning.

[0431] In one example, real-time training using reinforcement learning can be used, for example, as described hereinafter, to determine when to update a sound control pattern for, for example, sound control signal 109.

[0432] In another example, real-time training using reinforcement learning can be used to provide, for example, 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 to update NN150.

[0433] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, can be trained, configured, and / or updated during one or more stages, as described hereinafter for example.

[0434] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, can be trained, configured, and / or updated during a development stage, as described hereinafter for example.

[0435] In one example, an NN-based AAC system can be trained, for example during a development stage, to determine one or more MTFs and / or TTFs, such as STF parameters and / or settings, based on one or more predetermined and / or recorded signals of a vehicle setting assuming a certain TTF, such as STF.

[0436] In one example, an NN-based AAC system can be adjusted, for example using reinforcement learning, during on-site and / or real-time operation, for example for fine-tuning of the NN-based AAC system.

[0437] In some exemplary embodiments, the configuration of the NN of an NN-based AAC system, such as NN150, can be set as a production NN for production, for example as "frozen" after fine-tuning.

[0438] In some exemplary embodiments, an NN-based AAC system, such as AAC system 100, can be trained, configured, and / or updated during a production stage, as described hereinafter for example.

[0439] In one example, a frozen NN configuration can be used as an initial NN for an ANC system, for example during a production stage. For example, an ANC system, such as ANC system 100, can be configured with an initial NN setting that includes or is based on a copy of the "frozen" NN after fine-tuning.

[0440] In some exemplary aspects, an NN-based AAC system, such as AAC system 100, can be trained, configured, and / or updated during real-time use, for example, by a customer. In one example, the weights of NN150 can be updated, for example, slowly, using, for example, reinforcement learning. For example, the weights of NN150 can be updated to compensate for changes in one or more TFs during the life of the installed NN-based AA system, such as deformation, speaker degradation, etc.

[0441] In some exemplary aspects, NN150 can be trained based on one or more AAC parameters corresponding to the NN-based AAC system 100, as described hereinafter, for example.

[0442] In some exemplary aspects, the AAC parameters for training NN150 can include one or more of the parameters described above with respect to AAC information 129.

[0443] In one example, the AAC parameters for training NN150 for vehicle deployment of AAC system 100 can include, for example, one or more seat positions, seat occupancy, rear seat reclining, number of people in the vehicle, road type, speed, climate information, and / or any other additional or alternative parameters.

[0444] In some exemplary aspects, an NN-based AAC system, such as AAC system 100, can be implemented to provide a technical solution that can be configured to support AAC for multiple acoustic state scenarios, for example, to reduce noise within a noise control zone, such as noise control zone 110 (FIG. 2), as described hereinafter.

[0445] In one example, the AAC system can be configured to generate a noise cancellation pattern for a given acoustic state. However, depending on the case and / or scenario, for example in a real-time implementation, for example in the case of multiple acoustic state scenarios where one or more acoustic settings can be changed, there may be a need to provide a technical solution that supports the generation of the noise cancellation pattern with relatively high accuracy.

[0446] In some exemplary aspects, the NN-based AAC system, such as the AAC system 100, can be implemented to provide a technical solution that utilizes an NN, such as the NN 150, that can be trained based on a relatively large amount of measurement data. For example, in a vehicle implementation, it may be possible to easily access a relatively large amount of measurements corresponding to the acoustic configuration within the vehicle cabin.

[0447] In some exemplary aspects, the change in the measurement data corresponding to the acoustic configuration of the AAC system can be quantized to the final number of filters in order to support the realization and / or relatively easy method of training an NN-based AAC system, such as the AAC system 100.

[0448] In some exemplary aspects, the NN-based AAC system, such as the AAC system 100, can be configured to provide a technical solution that supports the rapid integration of one or more sensing capabilities, utilized as AAC support information, such as information 129, into an NN, such as the NN 150. In one example, the sensing capabilities may include, for example, camera, CAN data, and / or any other sensor information, such as input from one or more sensors within the vehicle cabin.

[0449] Refer to FIG. 3, which schematically shows a training scheme for training an NN-based AAC system according to some exemplary aspects.

[0450] In some example aspects, one or more settings of the NN150 (FIG. 1) can be trained according to one or more of the training schemes 300, such as some or all of the operations.

[0451] In some example aspects, as shown in FIG. 3, the NN350 can be trained to generate, for example, a sound control pattern 355, as described later.

[0452] In some example aspects, as shown in FIG. 3, the NN350 can be trained to generate a sound control pattern 355 based on, for example, AAC information 329, such as CAN data in a vehicle, a plurality of residual noise inputs 306, and / or a plurality of noise inputs 304.

[0453] In some example aspects, as shown in FIG. 3, the loss function 360 can be determined based on, for example, the sound control pattern 355 and a predetermined sound control pattern 365. For example, the predetermined sound control pattern 365 can include a pre-calibrated canceling noise pattern that can be set, for example, as described above, during the production and / or calibration phase of the NN-based ANC system.

[0454] In some example aspects, the loss function 360 can be determined based on, for example, the difference between the sound control pattern 355 and the predetermined sound control pattern 365, such as by subtracting the predetermined sound control pattern 365 from the sound control pattern 355. For example, the loss function 360 can include and / or represent a calibration error used to train the NN350.

[0455] Referring back to FIG. 1, in some example aspects, the controller 193 can be configured to process the input information 195 to determine, for example, a sound control pattern for controlling the sound within the sound control zone 110 based on, for example, a plurality of noise inputs 104, as described later.

[0456] In some exemplary embodiments, NN150 can be trained to generate an NN output 159 based on, for example, an NN input based on AAC configuration information 129, as will be described later.

[0457] In some exemplary embodiments, controller 193 can be configured to generate, for example, a sound control pattern for controlling acoustic transducer 108 based on, for example, NN output 159, as will be described later.

[0458] In some exemplary embodiments, input 191 can be configured to receive AAC configuration information 129 via, for example, a vehicle system bus including, for example, sound control zone 110, as described above.

[0459] In some exemplary embodiments, input 191 can be configured to receive AAC configuration information 129 via at least one of, for example, CAN bus information received via a vehicle CAN bus, A2B bus information received via a vehicle A2B bus, MOST bus information received via a vehicle MOST bus, wireless communication information received via a wireless communication link, or Ethernet bus information received via a vehicle Ethernet bus, as described above. In other embodiments, input 191 can be configured to receive AAC configuration information 129 via any other additional or alternative input interface, network interface, and / or bus interface.

[0460] In some exemplary embodiments, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 including information corresponding to the configuration of AAC within sound control zone 110, as will be described later.

[0461] In some example embodiments, NN150 can be trained to generate the NN output 159 based on AAC configuration information 129 that includes information representing the spectral distribution of an acoustic signal in at least one of the sound control zone 110 and / or the environment of the sound control zone 110, as described later, for example.

[0462] In some example embodiments, NN150 can be trained to generate the NN output 159 based on AAC configuration information 129 that includes information representing one or more parameters that affect the real-time configuration of the AAC within the sound control zone 110, as described later, for example.

[0463] In some example embodiments, NN150 can be trained to generate the NN output 159 based on AAC configuration information 129 that includes information representing one or more physical characteristics of the sound control zone 110, as described later, for example.

[0464] In some example embodiments, NN150 can be trained to generate the NN output 159 based on AAC configuration information 129 that includes information representing one or more acoustic characteristics of the sound control zone 110, as described later, for example.

[0465] In some example embodiments, NN150 can be trained to generate the NN output 159 based on AAC configuration information 129 that includes information from one or more information sources 120 different from the one or more acoustic sensors of the AAC system 100, such as the noise sensing location 105, the residual noise sensing location 107, and / or the monitoring location 103, as described later, for example.

[0466] In some exemplary embodiments, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes information from one or more information sources 120 independent of one or more acoustic sensors of the AAC system 100, such as, for example, the noise sensing location 105, the residual noise sensing location 107, and / or the acoustic sensors at the monitoring location 103, as described hereinafter.

[0467] In some exemplary embodiments, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes vehicle speed information corresponding to the speed of a vehicle that includes a sound control zone 110, as described hereinafter.

[0468] In some exemplary embodiments, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes engine information corresponding to the engine of a vehicle that includes a sound control zone 110, as described hereinafter.

[0469] In some exemplary embodiments, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes at least one of brake system information, road detection information, steering information, tire information, seat information, vehicle type information, and / or opening state information, as described hereinafter.

[0470] For example, the brake system information can include information corresponding to the brake system of a vehicle that includes a sound control zone 110, as described above.

[0471] For example, the road detection information can include information corresponding to the road detection system of a vehicle that includes a sound control zone 110, as described above.

[0472] For example, the steering information can include information corresponding to the steering system of a vehicle that includes a sound control zone 110, as described above.

[0473] For example, the tire information may include information corresponding to one or more tires of a vehicle including the sound control zone 110, for example, as described above.

[0474] For example, the seat information may include information corresponding to at least one of the position, for example, location and / or orientation, for example, angle, and / or occupancy of one or more seats of a vehicle including the sound control zone 110, for example, as described above.

[0475] For example, the vehicle type information may include information corresponding to the type of a vehicle including the sound control zone 110, for example, as described above.

[0476] For example, the opening state information may include information corresponding to the state of an opening of a vehicle including the sound control zone 110, for example, as described above.

[0477] In some exemplary aspects, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes passenger information corresponding to one or more passengers of a vehicle including the sound control zone 110, for example, as described hereinafter.

[0478] In some exemplary aspects, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes audio system information corresponding to an audio system of a vehicle including the sound control zone 110, for example, as described hereinafter.

[0479] In some exemplary aspects, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes climate information corresponding to at least one of the climate within the sound control zone 110 or the climate outside the sound control zone 110, for example, as described hereinafter.

[0480] In some example aspects, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes user position information corresponding to at least one position of a user's head or ear within sound control zone 110, as described later, for example.

[0481] In some example aspects, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes user identity information corresponding to a user's identity for controlling user preferences for sound control zone 110, as described later, for example.

[0482] In some example aspects, NN150 can be trained to generate an NN output 159 based on AAC configuration information 129 that includes vehicle system configuration information corresponding to the configuration of an operating mode of one or more vehicle systems of a vehicle that includes sound control zone 110, as described later, for example.

[0483] In some example aspects, NN150 can be trained to generate an 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, as described later, for example.

[0484] In some example aspects, NN150 can be trained to generate an NN output 159 based on an NN output that includes noise information based on AAC configuration information 129 and a plurality of noise inputs 104, as described later, for example.

[0485] In some example aspects, NN150 can be trained to generate an NN output 159 that includes, for example, settings of one or more AAC parameters, as described later, for example.

[0486] In some exemplary embodiments, the controller 193 may be configured to generate a sound control pattern for controlling the acoustic transducer 108 based on the setting of one or more AAC parameters, for example, as described later.

[0487] In some exemplary embodiments, the setting of one or more AAC parameters may include, for example, as described later, the setting of at least one of a prediction filter and / or a transfer function. In other embodiments, the setting of one or more AAC parameters may include the setting of any other additional or alternative parameters.

[0488] In some exemplary embodiments, the NN 150 may include a prediction filter (PF) NN 152 that may be trained to generate an NN output 159 including a PF setting based on, for example, as described later, an NN input.

[0489] In some exemplary embodiments, the controller 193 may be configured to generate a sound control pattern for controlling the acoustic transducer 108 by applying a PF configured according to the PF setting determined by, for example, as described later, the PF NN 152 to the noise information.

[0490] In some exemplary embodiments, the noise information may be based on, for example, as described later, a plurality of noise inputs 104.

[0491] In some exemplary embodiments, the controller 195 may be configured to generate a sound control pattern for controlling the acoustic transducer 108 based on input information 195 including, for example, as described later, noise error information, such as a residual noise input 106 representing a noise error at one or more error sensing positions.

[0492] In some exemplary embodiments, the controller 193 can be configured to determine the noise information to which PF is applied, for example, based on the noise error information 106 as described later.

[0493] In some exemplary embodiments, the PF NN 152 can be trained to generate an NN output 159 that includes, for example, a PF setting that may include a plurality of PF coefficients, as described later.

[0494] In some exemplary embodiments, the PF NN 152 can be trained to generate an NN output 159 that includes, for example, a PF setting that may include a weight vector representing a plurality of coefficients, as described later.

[0495] In other embodiments, the PF NN 152 can be trained to generate an NN output 159 that includes other additional or alternative information for defining the PF setting.

[0496] In some exemplary embodiments, the controller 193 may include a parameter extractor that can be configured to determine the extracted AAC parameter information, for example, based on sensor information from one or more sensors, as described later.

[0497] In some exemplary embodiments, the NN 150 can be trained to generate an NN output 159 based on an NN input that includes the extracted AAC parameter information, as described later.

[0498] In some exemplary embodiments, the parameter extractor may include a PCA extractor that can be configured to determine the extracted AAC parameter information, for example, in the time domain or frequency domain, based on, for example, principal component analysis (PCA) of the sensor information and / or based on PCA of the AAC information 129, as described later.

[0499] In some exemplary embodiments, the controller 193 may include a parameter extractor configured to determine the extracted AAC parameter information based on, for example, acoustic sensor information from one or more acoustic sensors, as described hereinafter.

[0500] In some exemplary embodiments, the PCA extractor may be configured to determine the extracted AAC parameter information based on, for example, the PCA of noise sensor information from one or more noise sensors, as described hereinafter.

[0501] In some exemplary embodiments, the PCA extractor may be configured to determine the extracted AAC parameter information based on, for example, the PCA of residual noise sensor information from one or more residual noise sensors, as described hereinafter.

[0502] In some exemplary embodiments, the PCA extractor may be configured to determine the extracted AAC parameter information based on, for example, the PCA of monitoring sensor information from one or more monitoring sensors, as described hereinafter.

[0503] In some exemplary embodiments, the PCA extractor may be configured to determine the extracted AAC parameter information based on, for example, the PCA of virtual sensor information from one or more virtual sensors, as described hereinafter.

[0504] In some exemplary embodiments, the PCA extractor may be configured to determine the extracted AAC parameter information including at least one of, for example, road type information, cabin state information, passenger position information, vehicle system state information, noise characteristic information, and / or noise state information, as described hereinafter. In other embodiments, the PCA extractor may be configured to determine the extracted AAC parameter information including any other additional or alternative type of information.

[0505] 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 travels.

[0506] For example, the cabin state information may include information corresponding to the state of the cabin of the vehicle including the sound control zone 110.

[0507] For example, the passenger position information may include information corresponding to the position of one or more passengers in the cabin of the vehicle including the sound control zone 110.

[0508] For example, the vehicle system state information may include information corresponding to the state of the system of the vehicle including the sound control zone 110.

[0509] For example, the noise characteristic information may include information representing one or more characteristics of the noise to be reduced.

[0510] In one example, the noise characteristic information may include information regarding the frequency band of the noise to be reduced, the level of the noise to be reduced, the type of the noise to be reduced, the source of the noise to be reduced, for example, one or more main peaks of the noise to be reduced with respect to one or more other nearby peaks or all ranges of frequencies, for example, a peak of ~200 hz > 10 db, a peak exceeding the energy level of 100 - 400 hz or 30 - 1000 hz, etc., and / or the level of increase over time at a specific frequency of the noise to be reduced, etc.

[0511] For example, the noise state information may include information corresponding to the state of the noise to be reduced.

[0512] In one example, the noise state information may include an indication of whether the noise to be reduced is a temporary noise or a non-temporary noise, etc.

[0513] In some exemplary aspects, the controller 193 can be configured to train the PF NN 152 based on, for example, one or more error sensing locations, such as one or more physical and / or virtual residual noise sensing locations 107, as described hereinafter.

[0514] In some exemplary aspects, the controller 193 can be configured to configure the PF NN 152 based on, for example, a transducer transfer function (TTF) setting of the TTF between a plurality of acoustic transducers 108 and one or more acoustic sensing locations, as described hereinafter.

[0515] In some exemplary aspects, the one or more acoustic sensing locations may include the physical sensing locations of physical acoustic sensors. For example, the controller 193 can be configured to configure the PF NN 152 based on, for example, a TTF setting of the TTF between the acoustic transducer 108 and an acoustic sensor physically located at the acoustic sensing location, as described hereinafter.

[0516] In some exemplary aspects, the one or more acoustic sensing locations may include the virtual sensing locations of virtual acoustic sensors. For example, the controller 193 can be configured to configure the PF NN 152 based on, for example, a TTF setting of the TTF between the acoustic transducer 108 and a virtual acoustic sensor at the virtual acoustic sensing location, as described hereinafter.

[0517] In some exemplary aspects, the one or more acoustic sensing locations may include residual noise sensing locations within the sound control zone 110. For example, the controller 193 can be configured to configure the PF NN 152 based on a TTF setting of the TTF between the acoustic transducer 108 and the residual noise sensing location 107.

[0518] In some exemplary embodiments, the one or more acoustic sensing positions may include a monitoring sensing position. For example, the controller 193 may be configured to configure the PF NN 152 based on the TTF setting of the TTF between the acoustic transducer 108 and the monitoring sensing position 103.

[0519] In some exemplary embodiments, the one or more acoustic sensing positions may include a noise sensing position outside the sound control zone 110. For example, the controller 193 may be configured to configure the PF NN 152 based on the TTF setting of the TTF between the acoustic transducer 108 and the noise sensing position 105, for example.

[0520] In some exemplary embodiments, the controller 193 may include a TTF NN 154 that may be trained to determine a TTF setting based on, for example, the AAC configuration information 129 as described later.

[0521] In some exemplary embodiments, the controller 193 may be configured to train the TTF NN 154 based on, for example, the sensed acoustic information corresponding to the one or more acoustic sensing positions, as described later.

[0522] For example, the sensed acoustic information corresponding to the one or more acoustic sensing positions may include the acoustic information sensed by the physical acoustic sensor at the physical acoustic sensing position.

[0523] For example, the sensed acoustic information corresponding to the one or more acoustic sensing positions may include the virtual acoustic information sensed by the virtual acoustic sensor at the virtual acoustic sensing position.

[0524] In some exemplary embodiments, the virtual acoustic sensor denoted by e at the virtual residual noise sensing position

Number

[0525] In some exemplary embodiments, the acoustic information of the virtual acoustic sensor e

Number

[0526] In some exemplary embodiments, the signal indicated by Pmtf, which represents the noise signal from the noise source sensed by the physical acoustic sensor P, can be, for example

Number

[0527] In some exemplary embodiments, the acoustic information of the virtual acoustic sensor e

Number

Number

Number

Number

[0528] In some exemplary embodiments, the PF setting, for example, the PF setting of PF NN152, may be determined according to a criterion that can be obtained based on, for example, the estimated acoustic information of the virtual acoustic sensor e

Number

[0529] In one example, the PF setting, for example, the PF setting of PF NN152, may be determined according to the following minimization criterion,

Number

Number

[0530] In another example, the PF setting, for example, the PF setting of PF NN152, may be determined according to the following minimization criterion,

Number

Number

[0531] In another example, the PF setting, for example, the PF setting of PF NN152, is based on the following minimization criterion [Number] and may be determined according to, where [Number] represents a transfer function between the acoustic transducer S and the physical acoustic sensor P, for example, a pre-calibrated transfer function.

[0532] In some exemplary aspects, the controller 193 may include a parameter extractor configured to determine the extracted AAC parameter information based on, for example, the AAC configuration information as described later.

[0533] In some exemplary aspects, the controller 193 may be configured to provide an NN input including the extracted AAC parameter information to the NN150, for example, as described later.

[0534] In some exemplary aspects, the controller 193 may be configured to provide an NN input including noise error information representing a noise error at one or more error sensing positions to the NN150, for example, as described later.

[0535] In some exemplary aspects, the controller 193 may be configured to provide an NN input to the NN150 based on the noise error information, for example, as described later.

[0536] In some exemplary embodiments, the controller 193 may be configured to determine settings of one or more sound control parameters by configuring the input to the NN 150 based on, for example, the AAC information 129 as described hereinafter, and to determine a sound control pattern based on the settings of the one or more sound control parameters.

[0537] In other embodiments, the controller 193 may be configured to determine settings of one or more sound control parameters based on any other additional or alternative criteria regarding the AAC information 129.

[0538] In some exemplary embodiments, the settings of the one or more sound control parameters may include, for example, PF settings for determining a sound control pattern based on, for example, a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106, as described hereinafter.

[0539] In some exemplary embodiments, the settings of the one or more sound control parameters may include a plurality of prediction filter coefficients for configuring the PF applied to determine a sound control pattern based on a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106, as described hereinafter.

[0540] In some exemplary embodiments, the settings of the one or more sound control parameters may include a prediction filter weight vector applied to determine a sound control pattern based on a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106, as described hereinafter.

[0541] In some exemplary embodiments, the settings of the one or more sound control parameters may include one or more path transfer functions, such as one or more TTFs, such as a speaker transfer function (STF), applied to determine a sound control pattern based on a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106, as described hereinafter.

[0542] In other embodiments, the setting of one or more sound control parameters may include the setting of one or more additional or alternative parameters, weights, coefficients, and / or functions that are applied to determine a sound control pattern based on a plurality of noise inputs 104 and / or a plurality of residual noise inputs 106.

[0543] In some exemplary embodiments, the controller 193 may determine the sound control signal 109, for example, by applying an estimation function and / or a prediction function to the noise input 104 and / or the residual noise input 106, as will be described later.

[0544] In some exemplary embodiments, the controller 193 may include an estimator (also referred to as a "prediction unit") configured to apply an estimation or prediction function to the noise input 104 and / or the residual noise input 106, as will be described later.

[0545] In some exemplary embodiments, the controller 193 may be configured to cause the estimator or prediction unit to utilize, for example, one or more prediction parameters for the estimation function based on, for example, the AAC information 129, as will be described later.

[0546] 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 based on, for example, the first AAC information 129.

[0547] 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 based on, for example, the second AAC information 129.

[0548] In some exemplary aspects, the prediction parameters may include, for example, as described hereinafter, weights, coefficients, functions, and / or any other additional or alternative parameters that are utilized to determine a sound control pattern.

[0549] In some exemplary aspects, the prediction parameters may include, for example, as described hereinafter, one or more transfer function parameters of an estimation or prediction function.

[0550] In one example, the prediction parameters may include one or more TTFs, such as STFs, applied by the controller 193 to determine a sound control pattern. In one example, the TTF, such as STF, 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, monitoring locations 103, and / or residual noise sensing locations 107.

[0551] In other aspects, the prediction parameters may include any other additional or alternative parameters.

[0552] In some exemplary aspects, the controller 193 may be configured to determine and / or set one or more TTFs, such as STFs, based on the AAC information 129, for example, as described hereinafter.

[0553] In some exemplary aspects, the controller 193 may be configured to determine and / or set one or more of the prediction parameters based on the AAC information 129, for example, as described hereinafter.

[0554] In some exemplary aspects, the NN 150 may be trained to generate an NN output 159 of the NN 150 that includes settings of one or more AAC parameters, and the controller 193 may be configured to generate a sound control signal based on the settings of the one or more AAC parameters, for example, as described hereinafter.

[0555] In some exemplary embodiments, the NN150 can be trained to generate an NN output 159 that includes, for example, as described hereinafter, the setting of a prediction filter, the setting of a transfer function, such as a TTF or STF, and / or the determination, generation, configuration, and / or setting of other optional parameters that can be used to control the acoustic transducer 108, such as a sound control signal 109.

[0556] In some exemplary embodiments, the NN150 can be trained to generate an NN output 159 for configuring the setting of a prediction filter, for example, based on the AAC information 129.

[0557] In some exemplary embodiments, the PF NN152 can be trained to generate a prediction filter setting for at least one prediction filter, for example, based on the AAC information 129, as described hereinafter.

[0558] In some exemplary embodiments, the PF NN152 can be trained to model, for example, in an improved manner, changes in the interior cabin of a vehicle.

[0559] In some exemplary embodiments, the PF NN152 can be implemented to provide a prediction filter setting to provide a technical solution configured to generate, for example, a sound control signal 109, for example, with reduced computational complexity, compared to an NN-based AAC system that utilizes an NN trained to directly output an acoustic sound control pattern configured to control sound within a sound control zone 110.

[0560] In one example, since it can be assumed that changes to the PF settings can occur relatively slowly, the workload of the PF NN152 can be relatively low, for example, in real-time operation. For example, the AAC controller 193 can be configured to sample the PF NN152, for example, every few cycles, and overwrite the existing PF settings with the updated PF settings output by the PF NN152.

[0561] In some exemplary embodiments, the prediction filter NN152 can be trained to output a plurality of PF coefficients, for example, in the form of a prediction filter weight vector, based on, for example, the AAC information 129, as will be described later.

[0562] In some exemplary embodiments, the controller 193 can be configured to determine the sound control signal 109, for example, by applying the PF to a plurality of reference noise inputs 104 according to the prediction filter coefficients determined by, for example, the PF NN152, as will be described later.

[0563] In some exemplary embodiments, the prediction filter NN152 can be trained to generate a prediction filter setting of the prediction filter, for example, based on, for example, a path transfer function between the acoustic transducer 108 and an acoustic sensing location, such as a residual noise sensing location 107, a monitoring location 103, and / or a noise sensing location 105, such as a TTF, such as an STF, as will be described later.

[0564] In some exemplary embodiments, the prediction filter NN152 can be trained to generate a prediction filter setting of the prediction filter, for example, based on, for example, a path transfer function between the acoustic transducer 108 and the residual noise sensing location 107, such as a TTF, such as an STF, as will be described later.

[0565] In some exemplary embodiments, the NN150 may include an STF NN154 trained to generate a TTF setting, such as an STF setting, corresponding to a path transfer function between the acoustic transducer 108 and the residual noise sensing location 107, as will be described later.

[0566] In some exemplary embodiments, the prediction filter NN152 can be trained to generate a prediction filter setting of the prediction filter, for example, based on, for example, a path transfer function between the acoustic transducer 108 and the noise sensing location 105, such as a TTF, such as an STF, as will be described later.

[0567] In some exemplary embodiments, the TTF(STF)NN154 can be trained to generate, for example, a TTF setting, such as an STF setting, corresponding to the path transfer function between, for example, the acoustic transducer 108 and the residual noise sensing location 107, as described hereinafter.

[0568] In some exemplary embodiments, the prediction filter NN152 can be trained to generate, for example, a prediction filter setting of the prediction filter based on, for example, the path transfer function between the acoustic transducer 108 and the monitoring location 103, such as a TTF, such as an STF, as described hereinafter.

[0569] In some exemplary embodiments, the NN150 can include an STF NN 154 that is trained to generate, for example, a TTF setting, such as an STF setting, corresponding to the path transfer function between the acoustic transducer 108 and the monitoring location 103, as described hereinafter.

[0570] In some exemplary embodiments, the TTF(STF)NN154 can be trained to generate, for example, a TTF setting, such as an STF setting, of the path transfer function based on, for example, the AAC information 129, as described hereinafter.

[0571] In some exemplary embodiments, the AAC controller 102 can be configured to reduce the AFB between, for example, the acoustic transducer 108 and one or more acoustic sensors of the AAC system 100, such as one or more of the reference noise acoustic sensor 119 and / or the residual noise sensor 121, as described hereinafter (also referred to as an "AFB controller", "AFB canceller", "feedback canceller (FBC)", "echo reducer", or "echo canceller") and may include an acoustic feedback (AFB) reducer.

[0572] In one example, the AFB reducer can be configured to reduce AFB, for example, as described below, between one or more acoustic transducers 108 and one or more reference noise acoustic sensors 119.

[0573] In another example, the AFB reducer can be configured to reduce AFB, for example, as described below, between one or more acoustic transducers 108 and one or more residual noise sensors 121.

[0574] In some exemplary embodiments, the controller 193 can be configured to generate an AFB reduction signal, for example, as described below, by applying an AFB setting to, for example, an acoustic transducer signal provided to one or more acoustic transducers 108, such as the acoustic transducer signal 109.

[0575] In some exemplary embodiments, the AFB setting can include, for example, a plurality of AFB reduction coefficients applied to the acoustic transducer signal to generate, for example, an AFB reduction signal.

[0576] In some exemplary embodiments, the controller 193 can be configured to determine, for example, as described below, a sound control pattern applied to the transducer 108m, for example, based on an AFB reduction signal.

[0577] In some exemplary embodiments, the controller 193 can be configured to determine, for example, as described below, a signal with reduced AFB corresponding to a specific acoustic sensor signal by subtracting an AFB reduction signal corresponding to the specific acoustic sensor signal from the specific acoustic sensor signal.

[0578] In one example, the controller 193 can be configured to determine an AFB reduction signal corresponding to the noise input 106 from the reference noise sensor 119 by applying an AFB setting corresponding to the reference noise sensor 119 and the acoustic transducer 108 to the acoustic transducer signal 109.

[0579] For example, the controller 193 may be configured to determine a signal in which the AFB corresponding to the noise input 106 from the reference noise sensor 119 is reduced by subtracting, for example, an AFB reduction signal corresponding to the noise input 106 from the noise input 106.

[0580] In another example, the controller 193 may be configured to determine an AFB reduction signal corresponding to the residual noise input 104 from the residual noise sensor 121 by applying, for example, an AFB setting corresponding to the residual noise sensor 121 and the acoustic transducer 108 to the acoustic transducer signal 109.

[0581] For example, the controller 193 may be configured to determine a signal in which the AFB corresponding to the residual noise input 104 from the residual noise sensor 121 is reduced by subtracting, for example, an AFB reduction signal corresponding to the residual noise input 104 from the residual noise input 104.

[0582] In some exemplary aspects, one or more operations and / or functions of the AFB reducer may be implemented by, for example, an AFB reduction NN, as described later.

[0583] In some exemplary aspects, the NN 150 may include an AFB reduction NN 155 that may be trained to generate an NN output 159 including an AFB setting, for example, based on an NN input, as described later.

[0584] In some exemplary aspects, the AFB setting may include, for example, a plurality of AFB coefficients applied to an acoustic transducer signal, such as the acoustic transducer signal 109, provided to, for example, one or more acoustic transducers 108, as described later.

[0585] In some exemplary embodiments, the AFB NN155 may be configured to provide a technical solution for adapting the AFB settings to changes in the acoustic medium between, for example, an acoustic transducer of the AAC system 100, such as the acoustic transducer 108, and an acoustic sensor of the AAC system 100, such as the reference noise sensor 119 and / or the residual noise sensor 121, as described hereinafter.

[0586] In some exemplary embodiments, the AFB NN155 may be trained to generate AFB settings, for example, based on the AAC configuration information 129, as described hereinafter.

[0587] In some exemplary embodiments, the controller 193 may be configured to determine an AAC profile based on the AAC information 129, for example, as described hereinafter.

[0588] In some exemplary embodiments, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on the AAC profile, for example, as described hereinafter.

[0589] In some exemplary embodiments, the controller 193 may be configured to set one or more parameters and / or attributes of the NN150 based on the AAC profile, for example, as described hereinafter.

[0590] In some exemplary embodiments, the AAC profile may include settings of one or more sound control parameters that may be used to determine a sound control pattern for the sound control signal 109, for example, as described hereinafter.

[0591] In some exemplary embodiments, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 based on settings of, for example, one or more sound control parameters, as described hereinafter.

[0592] In some exemplary embodiments, the memory 198 may be configured to store a plurality of AAC profiles 199, for example, as described hereinafter.

[0593] In some exemplary embodiments, the AAC profile 199 may include settings of one or more sound control parameters corresponding to the AAC operation configuration of the AAC system 100, for example, as described hereinafter.

[0594] In one example, the first AAC profile 199 may correspond to the first AAC operation configuration of the AAC system 100. According to this example, the first AAC profile 199 corresponding to the first AAC operation configuration of the AAC system 100 may include, for example, one or more sound control parameters. For example, the first setting of the one or more sound control parameters may be configured for sound control when the AAC system 100 operates under the first operating conditions.

[0595] In another example, the second AAC profile 199 may correspond to the second AAC operation configuration of the AAC system 100. According to this example, the second AAC profile 199 corresponding to the second AAC operation configuration of the AAC system 100 may include, for example, a second setting of one or more sound control parameters different from the first setting. For example, the second setting of the one or more sound control parameters may be configured for sound control when the AAC system 100 operates under second operating conditions different from the first operating conditions.

[0596] In some exemplary embodiments, the controller 193 may be configured to select an AAC profile selected from a plurality of AAC profiles 199 based on, for example, the AAC information 129, and to determine a sound control pattern for the sound control signal 109 based on, for example, the selected AAC profile, as described hereinafter.

[0597] In some exemplary aspects, the AAC profile 199 may include one or more user-based profiles corresponding to one or more users, as described hereinafter for example.

[0598] In some exemplary aspects, the user-based profile corresponding to a user may include settings of one or more sound control parameters based on, for example, the user's preferences, as described hereinafter for example.

[0599] In some exemplary aspects, the user-based profile may correspond to a user who is permitted to control, for example, as described above, the user preferences for the sound control zone 110.

[0600] In one example, the user-based profile may correspond to a user of the sound control zone 110. For example, the user-based profile of a vehicle driver may include settings of one or more sound control parameters based on the driver's preferences for the sound control zone 110 implemented with respect to the driver's seat of the vehicle.

[0601] In another example, the user-based profile may correspond to a first user for controlling the user preferences for the sound control zone 110 that can be used by a second user. For example, the user-based profile of a vehicle driver may include settings of one or more sound control parameters based on the driver's preferences for the sound control zone 110 implemented with respect to one or more passenger seats of the vehicle.

[0602] In some exemplary aspects, the AAC information 129 may include user identity information corresponding to the identity of a user, and the controller 193 may select a selected user-based profile from a plurality of AAC profiles 199 based on the user identity information.

[0603] In one example, the AAC profile 199 may include a user-based profile corresponding to the driver of the vehicle. For example, the controller 193 may be configured to identify identity information corresponding to the driver of the vehicle based on, for example, the AAC information 129 received from the vehicle's system. For example, the controller 193 may select a selected user-based profile corresponding to the driver from a plurality of AAC profiles 199 based on, for example, user identity information corresponding to the driver.

[0604] For example, the user-based profile corresponding to the driver may include information for defining settings of one or more sound control parameters for the sound control zone 110 based on the driver's preferences.

[0605] In one example, the user-based profile corresponding to the driver may include information for defining settings of one or more sound control parameters for the driver sound control zone 110 corresponding to the driver's seat. In another example, the user-based profile corresponding to the driver may include information for defining settings of one or more sound control parameters for the passenger sound control zone 110 corresponding to the seat of a passenger in the vehicle.

[0606] In some exemplary aspects, the controller 193 may be configured to determine a sound control pattern for the sound control signal 109 corresponding to the sound control zone 110 based on, for example, settings of one or more sound control parameters for the sound control zone 110 according to, for example, the user-based profile corresponding to the driver.

[0607] Referring to FIG. 4, which schematically shows an NN-based AAC system 400 according to some exemplary aspects.

[0608] For example, the AAC system 100 (FIG. 1) may include one or more elements of the AAC system 400 and / or may perform one or more operations and / or functions of the AAC system 400.

[0609] In some exemplary embodiments, as shown in FIG. 4, the AAC system 400 may include a PF NN 452, such as a PF DNN, trained to generate a PF setting of the prediction unit 460.

[0610] For example, the PF NN 152 (FIG. 1) may include one or more elements of the PF NN 452 and / or may perform one or more operations and / or functions of the PF NN 452.

[0611] In some exemplary embodiments, as shown in FIG. 4, the prediction unit 460 may be configured to predict and / or estimate an estimation function or prediction function 463 applied to one or more noise inputs 404 to generate, for example, a sound control pattern 409 as described later.

[0612] In some exemplary embodiments, as shown in FIG. 4, 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 one or more elements of the noise sensor 119 (FIG. 1) and / or may perform one or more operations and / or functions of the noise sensor 119 (FIG. 1).

[0613] In some exemplary embodiments, as shown in FIG. 4, the sound control pattern 409 may be configured to control at least one acoustic transducer 408. For example, the acoustic transducer 408 may include one or more elements of one or more acoustic transducers 108 (FIG. 1) and / or may perform one or more operations and / or functions of one or more acoustic transducers 108 (FIG. 1).

[0614] In some exemplary embodiments, as shown in FIG. 4, the PF NN 452 may be configured to generate a PF setting for the prediction unit 460 based on an NN input that may include, for example, AAC information 429 from one or more information sources 420, such as an environmental information source and / or any other information source. For example, the AAC information 429 may include the AAC information 129 from the information source 120 (FIG. 1).

[0615] In some exemplary embodiments, the PF NN 452 may be trained to generate a PF setting for the prediction unit 460 based on, for example, the AAC information 429.

[0616] In some exemplary embodiments, the noise input 404 and the AAC information 429 may be used as inputs to the NN-based AAC system 400.

[0617] In some exemplary embodiments, the output of the AAC system 400 may include a sound control pattern 409 that may be configured to reduce noise from the noise source 417, for example, at the error sensing location 421.

[0618] In some exemplary embodiments, the PF NN 452 may be trained and / or calibrated based on, for example, noise errors, as described later.

[0619] In some exemplary embodiments, the noise error at the error sensing location 407 may be based on, for example, the difference between the noise signal indicated by Y at the error sensing location 407 and the

Number

[0620] In some exemplary embodiments, the acoustic sound control pattern at the error sensing location 407

Number

[0621] In some exemplary embodiments, the noise signal Y at the error sensing position 407 may be based on, for example, the input response 415 of the acoustic channel between the noise source 417 and the error sensing position 407.

[0622] In some exemplary embodiments, the error sensing position 407 may include an error sensing position within a sound control zone, such as the sound control zone 110 (FIG. 2).

[0623] In one example, the noise error at the error sensing position 407 may be sensed by one or more residual noise sensors, such as the residual noise sensor 121 (FIG. 1). For example, a monitoring microphone, such as the error microphone 121 (FIG. 1), may be implemented to sense noise errors that may be caused by, for example, acoustic changes in the NN-based AAC system 400, such as acoustic changes within a vehicle cabin implementing the NN-based AAC system 400.

[0624] In another example, the noise error at the error sensing position 407 may be virtual residual noise that is virtually sensed by a virtual sensor at the error sensing position 407. For example, the noise error at the error sensing position 407 may be determined based on noise sensed by a monitoring sensor at a monitoring position, such as the monitoring position 103 (FIG. 1).

[0625] In some exemplary embodiments, the PF NN 452 may be trained based on an error signal 457 that may correspond to, for example, the noise error at the error sensing position 407.

[0626] In some exemplary embodiments, a residual noise sensor 421, such as an error microphone, may be configured to generate an error signal 457, for example, during the training of PF NN452. In other embodiments, the error signal 457 may be determined based on, for example, a virtual acoustic signal of a virtual acoustic sensor at an error sensing position 407, as described hereinafter.

[0627] In some exemplary embodiments, for example, during the training of PF NN452, a predetermined noise pattern 417 may be generated, a setting of predetermined AAC information 429 may be provided as an NN input to PF NN452, and an error signal 457 may be determined.

[0628] In some exemplary embodiments, for example, during the training of PF NN452, the determination of the error signal 457 may be repeated for, for example, a plurality of different predetermined noise patterns 417 and / or a plurality of different settings of predetermined AAC information 429.

[0629] In some exemplary embodiments, for example, during the training of PF NN452, PF NN452 may be trained based on a training criterion for minimizing or removing, for example, the error signal 457.

[0630] Refer to FIG. 5, which schematically shows an NN-based PF including PF NN552 for configuring the setting of the prediction unit 560 according to some exemplary embodiments.

[0631] For example, PF NN452 (FIG. 4) may include one or more elements of PF NN552, and / or may perform one or more operations and / or functions of PF NN552, and / or the prediction unit 460 (FIG. 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.

[0632] In some example aspects, the PF NN 552 can be trained to generate an NN output 503 that includes PF settings for the prediction unit 560.

[0633] For example, the PF NN 552 can be trained to generate an NN output 503 that includes a plurality of PF coefficients of a prediction function applied by the prediction unit 560. For example, the PF NN 552 can be trained to generate an NN output 503 that includes a weight vector representing a plurality of PF coefficients.

[0634] In some example aspects, as shown in FIG. 5, the prediction unit 560 can be configured to predict and / or estimate a prediction function 513 applied to the noise input 504 to generate, for example, a sound control pattern 509.

[0635] In some example aspects, as shown in FIG. 5, the PF NN 552 can be configured to generate PF settings for the prediction unit 560 based on, for example, the NN input 501, as described later.

[0636] In some example aspects, the NN input 501 can be configured to receive AAC information 529 from, for example, one or more information sources. For example, the AAC information 529 can include AAC information 129 (FIG. 1) from the information source 120 (FIG. 1).

[0637] In some example aspects, the AAC information 529 can include one or more vehicle / cabin parameters corresponding to settings within the vehicle cabin, for example, in a vehicle AAC implementation, as described above.

[0638] In one example, the AAC information 529 can include seat position information ("chair position") corresponding to the arrangement of the driver's seat and / or the arrangement of one or more passenger seats within the vehicle cabin, as described above.

[0639] In another example, the AAC information 529 may include temperature information corresponding to, for example, the temperature inside the vehicle cabin and / or the temperature outside the vehicle cabin as described above.

[0640] In other aspects, the AAC information 529 may include any other additional or alternative information, for example, as described above.

[0641] In some exemplary aspects, the PF NN 552 may be trained to generate an NN output 513 that includes a PF setting for the prediction unit 560, for example, based on the AAC information 529.

[0642] In some exemplary aspects, as shown in FIG. 5, 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.

[0643] In some exemplary aspects, as shown in FIG. 5, the plurality of layers of the PF NN 552 may be connected via a plurality of edges / nodes.

[0644] [[ID=ID=19]] In some exemplary aspects, as shown in FIG. 5, the input layer 551 may be configured to receive the AAC information 529.

[0645] In some exemplary aspects, as shown in FIG. 5, the output layer 557 may be configured to provide a PF setting for the prediction unit 560, for example, based on the AAC information 529.

[0646] In some exemplary aspects, as shown in FIG. 5,

Number

[0647] Refer to FIG. 6, which schematically shows a PF NN 652, according to some exemplary aspects.

[0648] For example, PF NN452 (FIG. 4) and / or PF NN552 (FIG. 5) may include one or more elements of PF NN652 and / or may perform one or more operations and / or functions of PF NN652.

[0649] In some exemplary aspects, PF NN652 may be trained to generate an NN output that includes a PF setting 603 for a prediction unit, such as prediction unit 550 (FIG. 5).

[0650] In some exemplary aspects, PF NN652 may be trained to generate an NN output that includes a PF setting 603 for configuring a prediction unit to predict and / or estimate a prediction function applied to a noise input 604 from, for example, one or more reference microphones.

[0651] In some exemplary aspects, as shown in FIG. 6, PF NN652 may be configured to receive an NN input that includes AAC information 629 from one or more AAC information sources. For example, AAC information 629 may include AAC information 129 (FIG. 1) from information source 120 (FIG. 1).

[0652] In one example, as shown in FIG. 6, AAC information 629 may include information corresponding to one or more physical parameters.

[0653] For example, as shown in FIG. 6, AAC information 629 may include pressure information indicated by P that corresponds to the pressure within the vehicle cabin.

[0654] For example, as shown in FIG. 6, AAC information 629 may include seat distance information indicated by D that corresponds to the setting of one or more seats within the vehicle cabin.

[0655] For example, as shown in FIG. 6, AAC information 629 may include speed information indicated by V that corresponds to the vehicle speed.

[0656] For example, as shown in FIG. 6, the AAC information 629 may include temperature information indicated by T corresponding to the temperature inside the vehicle cabin.

[0657] In other aspects, the AAC information 629 may include any other additional or alternative AAC configuration information, for example, as described above.

[0658] In some exemplary aspects, the PF NN 652 may be trained to generate, for example, a PF setting 603 for a prediction unit based on the AAC information 629.

[0659] In some exemplary aspects, as shown in FIG. 6, 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.

[0660] In some exemplary aspects, as shown in FIG. 6, the plurality of layers of the PF NN 652 may be connected via a plurality of edges.

[0661] In some exemplary aspects, as shown in FIG. 6, the input layer 651 may be configured to receive an NN input including the AAC information 629.

[0662] In some exemplary aspects, as shown in FIG. 6, 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.

[0663] In some exemplary aspects, as shown in FIG. 6, the estimation function 637 may be applied to the hidden layer 653.

[0664] In some exemplary aspects, as shown in FIG. 6, the estimation function 637 may include a non-linear function.

[0665] In other aspects, the estimation function 637 may include a linear function 637.

[0666] Referring again to FIG. 4, an NN-based AAC system, such as NN-based AAC system 400, may utilize a PF NN configured to process an NN input that includes the extracted AAC information of one or more extracted AAC parameters.

[0667] In some exemplary embodiments, the extracted AAC information of one or more extracted AAC parameters may be utilized in addition to, or instead of, for example, AAC information 429.

[0668] In some exemplary embodiments, the extracted AAC information may correspond to one or more AAC parameters that are not directly sensed and / or detected, for example, via one or more information sources 420.

[0669] In some exemplary embodiments, the extracted AAC information of the extracted AAC parameters may be extracted based on, for example, some or all of a plurality of noise inputs 404, as described hereinafter.

[0670] In some exemplary embodiments, the extracted AAC information of the extracted AAC parameters may be utilized to model one or more parameters that may not be sensed and / or detected via one or more information sources 420.

[0671] In some exemplary embodiments, the extracted AAC information of the extracted AAC parameters may include information extracted from some or all of AAC information 429.

[0672] In some exemplary embodiments, the extracted AAC information of the extracted AAC parameters may be implemented to provide a technical solution that may simplify the AAC architecture of the NN-based AAC system 400.

[0673] In some exemplary aspects, the extracted AAC information of the extracted AAC parameters can be implemented to provide a technical solution for reducing, for example, the computational workload of the NN-based AAC system 400.

[0674] Referring to FIG. 7, which schematically shows an NN-based AAC system 700 according to some exemplary aspects.

[0675] For example, the AAC system 100 (FIG. 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.

[0676] For example, the NN-based AAC system 700 may include one or more elements of the NN-based AAC system 400 (FIG. 4) and / or may perform one or more operations and / or functions of the NN-based AAC system 400 (FIG. 4).

[0677] In some exemplary aspects, as shown in FIG. 7, the AAC system 700 may include a PF NN 752 that is trained to generate, for example, as described above, a PF setting for the prediction unit 760.

[0678] In some exemplary aspects, as shown in FIG. 7, the prediction unit 760 may be configured to predict and / or estimate, for example, as described above, an estimation function or prediction function 763 that is applied to one or more noise inputs 704 to generate, for example, a sound control pattern 709.

[0679] In some exemplary aspects, as shown in FIG. 7, the noise input 704 may be provided, for example, as described above, by one or more acoustic sensors and may represent acoustic noise from a noise source.

[0680] For example, PF NN452 (FIG. 4) may include one or more elements of PF NN752 and / or may perform one or more operations and / or functions of PF NN752, and / or prediction unit 460 (FIG. 4) may include one or more elements of prediction unit 760 and / or may perform one or more operations and / or functions of prediction unit 760.

[0681] In some exemplary aspects, PF NN752 may be trained to generate PF settings for prediction unit 760 based on, for example, AAC information 729 from one or more AAC information sources. For example, AAC information 729 may include, for example, AAC information 129 (FIG. 1) from information source 120 (FIG. 1) as described above.

[0682] In some exemplary aspects, NN-based AAC system 700 may include AAC parameter extractor 720 configured to provide, for example, as described below, extracted AAC information 725 corresponding to one or more AAC parameters.

[0683] In some exemplary aspects, as shown in FIG. 7, AAC parameter extractor 720 may be configured to extract extracted AAC information 725 based on, for example, acoustic noise information from one or more noise inputs 704.

[0684] In some exemplary aspects, AAC parameter extractor 720 may be configured to extract extracted AAC information 725 based on, for example, AAC information 729 as some or all of an addition or alternative to the plurality of noise inputs 704.

[0685] In some exemplary aspects, AAC parameter extractor 720 may be configured to generate extracted AAC information 72, for example, as described below, by applying a predetermined residual noise extraction to, for example, the plurality of noise inputs 704 and / or AAC information 729.

[0686] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to generate the extracted AAC information 725 according to a predetermined reference noise extraction function that may be based on, for example, a principal component analysis (PCA) technique.

[0687] In other embodiments, any other additional or alternative techniques, methods, and / or mechanisms may be implemented to generate the extracted AAC information 725.

[0688] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine one or more principal components based on, for example, the PCA technique and based on, for example, a plurality of noise inputs 704 and / or AAC information 729.

[0689] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 based on, for example, sensor information 704 from one or more sensors, as described hereinafter.

[0690] In some exemplary embodiments, the AAC parameter extractor 720 may include a PCA extractor configured to determine the extracted AAC parameter information 725 based on, for example, PCA of the sensor information 704 in the time domain or the frequency domain.

[0691] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 based on, for example, PCA of noise sensor information from one or more noise sources.

[0692] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 based on, for example, PCA of one or more residual noise sensor information.

[0693] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 based on, for example, PCA of the monitoring sensor information from one or more monitoring sensors.

[0694] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 based on, for example, PCA of the virtual sensor information from one or more virtual sensors.

[0695] In some exemplary embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 including at least one of, for example, road type information, cabin state information, passenger position information, vehicle system state information, noise characteristic information, and / or noise state information, as described above.

[0696] In other embodiments, the AAC parameter extractor 720 may be configured to determine the extracted AAC parameter information 725 including any other additional or alternative type of information.

[0697] In some exemplary embodiments, as shown in FIG. 7, the PF NN 752 may be trained to generate PF settings for the prediction unit 760 based on, for example, a plurality of AAC parameters 725.

[0698] In some exemplary embodiments, as shown in FIG. 7, the PF NN 752 may be trained to generate PF settings for the prediction unit 760 based on, for example, the AAC information 729 and a plurality of AAC parameters 725.

[0699] In some exemplary embodiments, as shown in FIG. 7, the PF NN 752 may be configured to generate PF settings for the prediction unit 760 based on, for example, an NN input including the extracted AAC information 725 as an addition or alternative to one or more components of the AAC information 729.

[0700] In some exemplary aspects, the prediction unit 760 may determine a prediction function 763 based on a PF setting for the prediction unit 760.

[0701] In some exemplary aspects, a controller, such as the AAC controller 102 (FIG. 1) and / or the controller 193 (FIG. 1), may be configured to determine a sound control pattern 709, for example, by applying the prediction function 763 to a noise input 704 as described above.

[0702] In some exemplary aspects, as shown in FIG. 7, the sound control pattern 709 may be applied to one or more acoustic transducers, for example, to control sound within a sound control zone, as described above.

[0703] In some exemplary aspects, the PF NN 752 may be trained and / or calibrated, for example, based on a noise error, as described below.

[0704] In some exemplary aspects, the noise error at the error sensing location may be, for example, based on the difference between the noise signal indicated by Y at the error sensing location and the

Number

[0705] In some exemplary aspects, as shown in FIG. 7, the acoustic sound control pattern at the error sensing location

Number

[0706] In some exemplary embodiments, the PF NN 752 can be trained, for example, as described above, based on an error signal that can correspond to a noise error at an error sensing location.

[0707] In some exemplary embodiments, during training of the PF NN 752, for example, a predetermined noise pattern can be generated, and a predetermined setting of the AAC information 729 and a predetermined setting of the extracted AAC information 725 can be provided as an NN input to the PF NN 752, and an error signal can be determined.

[0708] In some exemplary embodiments, during training of the PF NN 752, for example, the determination of the error signal can be repeated for, for example, a plurality of different predetermined noise patterns, a plurality of different predetermined settings of the AAC information 729, and / or a plurality of different predetermined settings of the extracted AAC information 725.

[0709] In some exemplary embodiments, during training of the PF NN 752, for example, the PF NN 752 can be trained based on a training criterion for minimizing or removing the error signal.

[0710] Refer to FIG. 8, which schematically shows an NN-based PF including the PF NN 852 for configuring the settings of the prediction unit 860 according to some exemplary embodiments.

[0711] For example, the PF NN 752 (FIG. 7) can include one or more elements of the PF NN 852, and / or can perform one or more operations and / or functions of the PF NN 852, and / or the prediction unit 760 (FIG. 7) can include one or more elements of the prediction unit 860, and / or can perform one or more operations and / or functions of the prediction unit 860.

[0712] In some exemplary embodiments, the PF NN 852 can be trained to generate an NN output 813 that includes PF settings for the prediction unit 860.

[0713] In some exemplary aspects, as shown in FIG. 8, the prediction unit 860 may be configured to predict and / or estimate a prediction function 803 that is applied to the noise input 804 to generate, for example, a sound control pattern 809.

[0714] In some exemplary aspects, as shown in FIG. 8, the PF NN 852 may be configured to generate a PF setting for the prediction unit 860, for example, based on the NN input 801 as described later. For example, the NN output 813 may include a plurality of PF coefficients for configuring the prediction function 803 for the prediction unit 860, as described above.

[0715] In some exemplary aspects, the NN input 801 may be configured to receive AAC information 829 from, for example, one or more information sources. For example, the AAC information 829 may include the AAC information 129 (FIG. 1) from the information source 120 (FIG. 1).

[0716] In some exemplary aspects, the AAC information 829 may include one or more vehicle / cabin parameters corresponding to settings within the vehicle cabin in a vehicle AAC implementation, for example, as described above.

[0717] In other aspects, the AAC information 829 may include any other additional or alternative information, for example, as described above.

[0718] In some exemplary aspects, as shown in FIG. 8, the PF NN 852 may be configured to generate a PF setting for the prediction unit 860 based on extracted feature information that may be extracted based on sensor information from, for example, one or more sensors, such as one or more physical and / or virtual sensors, such as a noise sensor, a monitoring sensor, and / or a residual noise sensor, as described later.

[0719] In some exemplary embodiments, the NN input 801 can be configured to receive, for example, the AAC information 825 extracted from the AAC parameter extractor 820, as will be described later.

[0720] In some exemplary embodiments, the AAC parameter extractor 820 can be configured to extract the AAC information 825 extracted from, for example, the AAC information 829 and / or the noise input 804. For example, the AAC parameter extractor 820 may include one or more elements of the AAC parameter extractor 720 (FIG. 7), and / or may perform one or more operations and / or functions of the AAC parameter extractor 720 (FIG. 7).

[0721] In some exemplary embodiments, as shown in FIG. 8, the AAC parameter extractor 820 can be configured to extract the extracted AAC information 825 based on, for example, the PCA information 836 that can be determined based on the noise input 804, as will be described later.

[0722] In some exemplary embodiments, as shown in FIG. 8, the noise input 804 can be converted from the time domain to the frequency domain using, for example, the fast Fourier transform (FFT) 832.

[0723] In some exemplary embodiments, as shown in FIG. 8, the PCA information 836 may include one or more principal components that can be determined, for example, in the frequency domain by applying PCA to the noise input 804. In other embodiments, the PCA information 836 may include one or more principal components that can be determined, for example, in the time domain by applying PCA to the noise input 804.

[0724] In some exemplary embodiments, as shown in FIG. 8, the AAC parameter extractor 820 can be configured to determine the extracted AAC information 825 based on, for example, one or more principal components 836.

[0725] In some exemplary embodiments, the PF NN 852 can be trained to generate an NN output 803 that includes PF settings for the prediction unit 860, based on, for example, the AAC information 829 and the extracted AAC information 825.

[0726] In some exemplary embodiments, as shown in FIG. 8, the PF NN 852 can include a plurality of layers including an input layer 851, a plurality of hidden layers 852, and an output layer 857.

[0727] In some exemplary embodiments, as shown in FIG. 8, the plurality of layers of the PF NN 852 can be connected via a plurality of edges / nodes.

[0728] In some exemplary embodiments, as shown in FIG. 8, the input layer 851 can be configured to receive the AAC information 829 and the extracted AAC information 825.

[0729] In some exemplary embodiments, as shown in FIG. 8, the output layer 857 can be configured to provide PF settings to the prediction unit 860, based on, for example, the AAC information 829 and the extracted AAC information 825.

[0730] In some exemplary embodiments, the AAC parameter extractor 820 can be configured to generate the extracted AAC information 825 that includes, for example, the extracted road type information, based on, for example, the noise input 804 and / or the AAC information 829, as described later.

[0731] In some exemplary embodiments, the AAC parameter extractor 820 can be configured to generate the extracted AAC information 825 that includes any other additional or alternative extracted AAC information, as described above.

[0732] In some exemplary embodiments, the AAC parameter extractor 820 may be configured to determine road type information based on, for example, road noise patterns corresponding to a plurality of road types and / or a plurality of reference noise patterns, as described hereinafter.

[0733] Refer to FIG. 9, which schematically shows an AAC parameter extraction scheme 900 for configuring the extraction of AAC information according to some exemplary embodiments.

[0734] In some exemplary embodiments, the AAC parameter extraction scheme 900 may be configured to extract road type parameters, for example, as described hereinafter. In other embodiments, the AAC parameter extraction scheme 900 may be configured to extract any other additional or alternative AAC parameter information, for example, as described above.

[0735] In some exemplary embodiments, as shown in FIG. 9, the AAC parameter extraction scheme 900 may include an AAC parameter extractor 920. For example, the AAC parameter extractor 820 (FIG. 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.

[0736] In some exemplary embodiments, the AAC parameter extractor 920 may be configured to extract AAC parameter information including, for example, road type parameters, based on noise input 904 that may be received from one or more acoustic sensors 919, such as reference noise sensors and / or monitoring sensors, such as reference noise sensor 119 (FIG. 1) and / or monitoring sensors at monitoring sensing locations 103 (FIG. 2).

[0737] In some exemplary embodiments, as shown in FIG. 9, the AAC parameter extractor 920 may extract AAC parameter information including, for example, road type parameters, based on, for example, a 2D principal component reference map 930.

[0738] In some exemplary embodiments, the 2D principal component reference map 930 may include a plurality of 2D principal components 932 corresponding to respective ones of a plurality of AAC parameter options, such as a plurality of road types.

[0739] In some exemplary embodiments, the 2D principal component reference map 930 may be determined during training of an AAC system, such as the AAC system 700 (FIG. 7).

[0740] In some exemplary embodiments, the 2D principal component reference map 930 may be determined, for example, by applying a predetermined noise pattern for a plurality of AAC parameter options, such as a road noise pattern corresponding to a plurality of road types, from a database (DB) 904 of predetermined AAC parameter noise patterns, such as road noise patterns, to the reference noise sensor 919. For example, a particular 2D principal component 932 corresponding to a respective particular road type may be determined by applying a predetermined noise pattern from a DB 940 corresponding to the particular road type to the reference noise sensor 919.

[0741] In some exemplary embodiments, as shown in FIG. 9, 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.

[0742] In some exemplary embodiments, as shown in FIG. 9, 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.

[0743] In some exemplary aspects, as shown in FIG. 9, the AAC parameter extractor 920 may determine AAC parameter information including, for example, road type parameters, based on the correlation between one or more principal components 936 from the noise input 904 and the plurality of 2D principal components 936 in the 2D principal component reference map 930.

[0744] Referring again to FIG. 4, in some exemplary aspects, the prediction filter NN452 may be trained to generate a prediction filter setting for the prediction filter 460 based on, for example, a path transfer function between the acoustic transducer 408 and one or more acoustic sensing positions, such as the error sensing position 407, for example, the TTF or STF.

[0745] In some exemplary aspects, the prediction filter NN452 may be trained to generate a prediction filter setting for the prediction filter 460 based on, for example, the path transfer function between the acoustic transducer 408 and the error sensing position 407, as described hereinafter.

[0746] In other aspects, the prediction filter NN452 may be trained to generate a prediction filter setting for the prediction filter 460 based on, for example, one or more path transfer functions between the acoustic transducer 408 and one or more additional or alternative acoustic sensing positions, such as one or more noise sensing positions, one or more residual noise sensing positions, and / or one or more monitoring sensing positions, as described above.

[0747] In some exemplary aspects, the prediction filter NN452 may be trained to generate a prediction filter setting for the prediction filter 460 based on, for example, the settings of the TTF, such as the STF, TTF(STF) between the acoustic transducer 408 and one or more acoustic sensing positions, such as the error sensing position 407, as described hereinafter.

[0748] In some exemplary embodiments, the TTF(STF) setting of the TTF(STF) between the acoustic transducer 408 and the acoustic sensing position can be determined using, for example, TTF(STF)NN, for example TTF(STF)NN154 (FIG. 1), as described hereinafter.

[0749] In some exemplary embodiments, the TTF(STF) setting of the TTF(STF) between the acoustic transducer 408 and the error sensing position 407 can be determined using, for example, TTF(STF)NN, for example TTF(STF)NN154 (FIG. 1), as described hereinafter.

[0750] In some exemplary embodiments, TTF(STF)NN, for example TTF(STF)NN154 (FIG. 1), can be trained to generate, for example, the TTF(STF) setting of TTF(STF) based on, for example, AAC information 429, as described hereinafter.

[0751] Refer to FIG. 10, which schematically shows an NN-based AAC system 1000 according to some exemplary embodiments. For example, the AAC system 100 (FIG. 1) may include one or more elements of the AAC system 1000 and / or may perform one or more operations and / or functions of the AAC system 1000.

[0752] In some exemplary embodiments, as shown in FIG. 10, the NN-based AAC system thousands 1000 may include a PF NN 1052 that can be trained to generate, for example, a PF setting for the prediction unit 1060, as described above.

[0753] In some exemplary embodiments, as shown in FIG. 10, the prediction unit 1060 may be configured to predict and / or estimate, for example, an estimation function or a prediction function 1063 that is applied to one or more noise inputs 1004 to generate, for example, a sound control pattern 1090, as described above.

[0754] In some exemplary aspects, the sound control pattern 1009 can be applied, for example, as described above, to control the acoustic transducer 1008 to control sound in a sound control zone.

[0755] In some exemplary aspects, as shown in FIG. 10, the noise input 1004 can be provided by one or more acoustic sensors, for example, as described above, and can represent acoustic noise from a noise source.

[0756] For example, the PF NN 452 (FIG. 4) may include one or more elements of the PF NN 1052 and / or may perform one or more operations and / or functions of the PF NN 1052, and / or the prediction unit 460 (FIG. 4) may include one or more elements of the prediction unit 1060 and may perform one or more operations and / or functions of the prediction unit 1060.

[0757] In some exemplary aspects, as shown in FIG. 10, the PF NN 1052 can be trained, for example, as described above, to generate a PF setting for the prediction unit 1060 based on, for example, AAC information from one or more AAC information sources and / or based on the extracted AAC information from the AAC parameter extractor.

[0758] In some exemplary aspects, the PF NN 1052 can be trained, for example, as described hereinafter, to generate a PF setting for the prediction unit 1060 based on, for example, the TTF (STF) 1031 between the acoustic transducer 1008 and one or more acoustic sensing positions.

[0759] In some exemplary aspects, the PF NN 1052 can be trained, for example, as described hereinafter, to generate a prediction filter setting of the prediction filter 1060 based on the path transfer function between the acoustic transducer 1008 and the error sensing position 1007.

[0760] In other aspects, PF NN 1052 can be trained to generate the prediction filter settings of prediction filter 1060 based on one or more path transfer functions between acoustic transducer 1008 and one or more additional or alternative acoustic sensing locations, such as one or more noise sensing locations, one or more residual noise sensing locations, and / or one or more monitoring sensing locations.

[0761] In one example, PF NN 1052 can be trained to generate the prediction filter settings of prediction filter 1060 based on the path transfer function between acoustic transducer 1008 and monitoring sensing location 103 (FIG. 2), which can be used, for example, to estimate the virtual error at error sensing location 1007 as described above.

[0762] In some exemplary aspects, as shown in FIG. 10, PF NN 1052 can be trained to generate PF settings for prediction unit 1060 based on, for example, the TTF(STF) 1031 between acoustic transducer 1008 and, for example, error sensing location 1007 within a sound control zone.

[0763] In some exemplary aspects, as shown in FIG. 10, PF NN 1052 can be trained to learn the TTF NN space between, for example, one or more transducers 1008 and one or more acoustic sensing locations, such as error sensing location 1007, monitoring location (FIG. 2), and / or noise sensing location 105 (FIG. 2), as described hereinafter.

[0764] In some exemplary aspects, the error sensing location 1007 may include an error sensing location within a sound control zone, such as sound control zone 110 (FIG. 2). For example, a noise error at the error sensing location 1007 may be sensed by one or more residual noise sensors, such as residual noise sensor 121 (FIG. 1). For example, a monitoring microphone, such as error microphone 121 (FIG. 1), may be implemented to sense noise errors that may be caused by, for example, acoustic changes in an NN-based AAC system 1000, such as acoustic changes within a vehicle cabin implementing the NN-based AAC system 1000.

[0765] In some exemplary aspects, as shown in FIG. 10, the AAC system 1000 may include a TTF(STF)NN 1054 that is trained to generate TTF(STF) setting information 1096 representing an estimated TTF(STF) setting between an acoustic transducer 1008 and one or more acoustic sensing locations, such as error sensing location 1007, as described hereinafter. For example, the TTF(STF)NN 154 (FIG. 1) may include one or more elements of the TTF(STF)NN 1054 and / or may perform one or more operations and / or functions of the TTF(STF)NN 1054.

[0766] In some exemplary aspects, the TTF(STF)NN 1154 and / or the PF NN 1052 may be trained individually. In other aspects, the TTF(STF)NN 1154 and / or the PF NN 1052 may be trained in parallel.

[0767] In some exemplary aspects, the TTF(STF)NN 1054 may be trained and / or calibrated based on noise errors at one or more acoustic sensing locations, such as error sensing location 1007, as described hereinafter.

[0768] In some exemplary embodiments, an AAC system, such as AAC system 1000, may utilize a TTF(STF)NN, such as TTF(STF)NN 1054, to estimate the TTF(STF) between an acoustic transducer 1008 and one or more acoustic sensing positions, such as error sensing position 1007, for example, to provide a more accurate estimate of the actual TTF(STF).

[0769] In some exemplary embodiments, the noise error at the error sensing position 1007 may be based on, for example, the difference between the noise signal at the error sensing position 1007 and the acoustic sound control pattern at the error sensing position 1007.

[0770] In some exemplary embodiments, the acoustic sound control pattern at the error sensing position 1007 may be based on the sound control pattern 1009 and the TTF(STF) 1031 between the acoustic transducer 1008 and the error sensing position 1007.

[0771] In some exemplary embodiments, the noise signal at the error sensing position 1007 may be based on the impulse response of the acoustic channel between the noise source and the error sensing position 1007.

[0772] In some exemplary embodiments, the TTF(STF)NN 1054 may be trained to generate, for example, TTF(STF) setting information 1096 for a plurality of acoustic transducer sensor pairs within the AAC system 1000. For example, an acoustic transducer sensor pair, such as each acoustic transducer sensor pair, may include different combinations of an acoustic transducer and an acoustic sensor of the AAC system 1000, such as an error microphone, a monitoring microphone, a virtual sensor, and / or a noise microphone.

[0773] In some exemplary aspects, the TTF(STF) settings for each scenario and each acoustic transducer sensor pair can be implemented to provide an improved, e.g., optimal, technical solution that supports TTF(STF) settings.

[0774] In some exemplary aspects, the TTF(STF) NN1054 can be trained based on an error signal 1057 that can, for example, correspond to a noise error at the error sensing location 1007.

[0775] In some exemplary aspects, a residual noise sensor 1021, e.g., an error microphone, can be configured to generate an error signal 1057, for example, during the training of the TTF(STF) NN1054.

[0776] In some exemplary aspects, for example, as described later, during the training of the TTF(STF) NN1054, a predetermined noise pattern may be generated, a setting of predetermined AAC information may be provided as an NN input to the TTF(STF) NN1054, and an error signal 1057 may be determined.

[0777] In some exemplary aspects, for example, as described later, during the training of the TTF(STF) NN1054, the determination of the error signal 1057 can be repeated for, for example, a plurality of different predetermined noise patterns and / or a plurality of different settings of predetermined AAC information.

[0778] In some exemplary aspects, during the training of the TTF(STF) NN1054, the TTF(STF) NN1054 can be trained based on a training criterion, for example, as described later, to minimize or remove the error signal 1057.

[0779] Refer to FIG. 11, which schematically shows a training scheme 1100 for training the TTF(STF) NN1154 according to some exemplary aspects.

[0780] For example, TTF(STF)TF NN1054 (FIG. 10) may include one or more elements of TTF(STF)NN1154 and / or may perform one or more operations and / or functions of TTF(STF)NN1154.

[0781] In some exemplary aspects, TTF(STF)NN1154 may be trained to generate a TTF(STF) setting 1155 of TTF(STF) between an acoustic transducer 1108 and an acoustic sensing location 1107, such as an error sensing location, a monitoring sensing location, and / or a noise sensing location.

[0782] In some exemplary aspects, TTF(STF)NN1154 may be trained to generate a TTF(STF) setting 1155, for example, based on AAC information 1129. For example, AAC information 1129 may include AAC information 129 (FIG. 1) from an information source 120 (FIG. 1).

[0783] In some exemplary aspects, as shown in FIG. 11, TTF(STF)NN1154 may be trained based on a sensed acoustic signal 1157 at an acoustic sensing location 1107. In one example, signal 1157 may include an error signal from an error microphone 1121 at an error sensing location 1107.

[0784] In some exemplary aspects, the error signal 1157 may be based on, for example, a noise difference between a first signal 1132 and a second signal 1134, as described later.

[0785] In some exemplary aspects, the first signal 1132 is applied to the acoustic transducer 1108 and may be based on a sound control pattern 1123 from a noise generator 1120 that can be received by an error microphone 1121 at an acoustic sensing location 1107, such as an error sensing location 1107, via an actual TTF(STF) between the transducer 1108 and the acoustic sensing location 1107, such as an error sensing location 1107.

[0786] In some exemplary embodiments, the second signal 1134 may be generated by applying a TTF(STF) setting 1155, generated for example by TTF(STF)NN1154, to a sound control pattern 1123 from the noise generator 1120.

[0787] In some exemplary embodiments, during training of, for example, TTF(STF)NN1154, a predetermined noise pattern 1123 may be generated by the noise generator 1120, a setting of predetermined AAC information 1129 may be provided as an NN input to TTF(STF)NN1154, and an error signal 1157 may be determined.

[0788] In some exemplary embodiments, during training of, for example, TTF(STF)NN1154, the determination of a sensed acoustic signal 1157, for example an error signal 1157, may be repeated for, for example, a plurality of different predetermined noise patterns 1123 and / or a plurality of different settings of predetermined AAC information 1129.

[0789] In some exemplary embodiments, during training of, for example, TTF(STF)NN1154, TTF(STF)NN10154 may be trained based on a training criterion to minimize or remove a sensed acoustic signal 1157, for example an error signal 1157.

[0790] In some exemplary embodiments, the output of TTF(STF)NN1154 may include a vector of second-order transfer functions. During training, for example, this vector of second-order transfer functions may be compared to an estimated TTF(STF) based on, for example, signal 1132.

[0791] Referring to FIG. 12, which schematically shows an NN-based AAC system 1200 according to some exemplary embodiments.

[0792] For example, the AAC system 100 (FIG. 1) may include one or more elements of the AAC system 1200 and / or may perform one or more operations and / or functions of the AAC system 1200.

[0793] In some exemplary embodiments, the AAC system 1200 may include a multiple-input multiple-output (MIMO) AAC system configured to reduce noise from a plurality of noise sources 1220, for example as described below.

[0794] In some exemplary embodiments, as shown in FIG. 12, the NN-based AAC system 1200 may include a PF NN 1252 trained to generate a PF setting for the prediction unit 1260.

[0795] In some exemplary embodiments, as shown in FIG. 12, the prediction unit 1260 may be configured to predict and / or estimate an estimation function or prediction function 1263 applied to one or more noise inputs 1204, for example, during an operating mode of the AAC system 1200, to generate a sound control pattern 1209.

[0796] In some exemplary embodiments, the sound control pattern 1209 may be applied to control one or more acoustic transducers 1208 to control sound in a sound control zone, for example as described above.

[0797] In some exemplary embodiments, as shown in FIG. 12, the noise input 1204 may be provided by one or more acoustic sensors and may represent acoustic noise from a plurality of noise sources 1220, for example as described above.

[0798] For example, PF NN452 (Figure 4) may include one or more elements of PF NN1052, and / or may perform one or more operations and / or functions of PF NN1252, and / or, the prediction unit 460 (Figure 4) may include one or more elements of the prediction unit 1260 and may perform one or more operations and / or functions of the prediction unit 1260.

[0799] In some exemplary aspects, PF NN1252 may be trained to generate a PF setting for the prediction unit 1260, for example, as described above, based on AAC information from one or more AAC information sources and / or based on the extracted AAC information from the AAC parameter extractor.

[0800] For example, as shown in Figure 12, PF NN1252 may be trained, for example, using TTF(STF) in a training loop, for example, according to forward propagation during a training session.

[0801] Refer to Figure 13, which schematically shows a graph 1320 of the performance versus time of active noise reduction (ANR) of an NN-based AAC system according to some exemplary aspects.

[0802] In one example, the AAC system 100 (Figure 1), the AAC system 400 (Figure 4), the AAC system 800 (Figure 8), and / or the AAC system 1200 (Figure 12) may be configured to provide a technical solution that supports ANR performance that is approximately the same as or better than the ANR performance of the graph 1320.

[0803] In some exemplary aspects, as can be seen from the graph 1320, the NN-based AAC system may achieve effective ANR after a relatively short period, for example, less than 1 second.

[0804] Refer to FIG. 14A which schematically shows an NN-based controller 1400 according to some exemplary aspects. In some aspects, AAC controller 102 (FIG. 1) and / or controller 193 may perform one or more functions and / or operations of, for example, controller 1400.

[0805] In some exemplary aspects, controller 1400 may receive AAC information 1429 including, for example, AAC information 129 (FIG. 1).

[0806] In some exemplary aspects, controller 1400 may receive a plurality of inputs 1404 including, for example, input 104 (FIG. 1) representing acoustic noise at a plurality of predetermined noise sensing positions, such as position 105 (FIG. 2). Controller 1400 may generate a sound control signal 1412 for controlling at least one acoustic transducer 1414, such as acoustic transducer 108 (FIG. 1).

[0807] In some exemplary aspects, controller 1400 may include an NN-based estimator (“prediction unit”) 1410 for estimating sound control signal 1412 based on, for example, NN input 1408 which may be obtained based on at least input 1404.

[0808] In some exemplary aspects, NN-based estimator 1410 may include one or more elements of NN150 (FIG. 1) and / or may perform one or more operations and / or functions of NN150 (FIG. 1).

[0809] In some exemplary aspects, NN-based estimator 1410 may include one or more elements of PF NN152 (FIG. 1) and / or may perform one or more operations and / or functions of PF NN152 (FIG. 1).

[0810] In some exemplary aspects, the NN-based estimator 1410 may include one or more elements of the PF NN 452 (FIG. 1) and / or the PF 460 (FIG. 4), and / or may perform one or more operations and / or functions of the PF NN 452 (FIG. 1) and / or the PF 460 (FIG. 4).

[0811] In some exemplary aspects, the NN-based estimator 1410 may include one or more elements of the TTF (STF) NN 154 (FIG. 1), and / or may perform one or more operations and / or functions of the TTF (STF) NN 154 (FIG. 1).

[0812] In some exemplary aspects, the NN-based estimator 1410 may include one or more elements of the TTF (STF) NN 1054 (FIG. 10), and / or may perform one or more operations and / or functions of the TTF (STF) NN 1054 (FIG. 10).

[0813] In some exemplary aspects, the NN-based estimator 1410 may be trained to generate the signal 1412, for example, as described above, based on, for example, the AAC information 1429.

[0814] In some exemplary aspects, the NN-based estimator 1410 may include an NN, such as the NN 150 (FIG. 1), that is trained to directly generate the signal 1412 based on, for example, the NN input 1408 and the AAC information 1429, for example, as described above.

[0815] In some exemplary aspects, the NN-based estimator 1410 may include a PF NN, such as the PF NN 452 (FIG. 4), and a PF, such as the PF 460 (FIG. 4). For example, the PF NN, such as the PF NN 452 (FIG. 4), may be trained to generate the PF settings of the PF, such as the PF 460, and the PF, such as the PF 460 (FIG. 4), may generate the signal 1412 by applying the PF settings to the NN input 1408, for example, as described above.

[0816] In some example aspects, as shown, for example, in FIG. 14A, the controller 1400 may include an extractor 1406 for extracting a plurality of reference acoustic patterns, such as non-identical reference acoustic patterns, from the input 1404. According to these aspects, the input 1408 may include one or more of the plurality of reference acoustic patterns.

[0817] In some example aspects, the controller 1400 may generate a signal 1412 configured to reduce and / or remove noise generated by one or more noise sources, as described above, for example.

[0818] In some example aspects, the controller 1400 may generate a sound control signal 1412 configured to reduce and / or remove the noise energy and / or wave amplitude of one or more sound patterns within a sound control zone, but the noise energy and / or wave amplitude of one or more other sound patterns may not be affected within the sound control zone.

[0819] In some example aspects, the sound control signal 1412 may be configured to reduce and / or remove noise generated by one or more vehicle systems, as described above, for example.

[0820] In some example aspects, the feature extractor 1406 may determine, update, and / or adjust, for example in real time, the setting of at least one acoustic pattern extractor parameter based on, for example, the AAC information 1429, as described above, and may be configured to determine a plurality of reference acoustic patterns for the NN input 1408 based on the acoustic pattern extractor parameters.

[0821] In other aspects, the controller 1400 may not include the extractor 1406. Thus, the NN input 1408 may include the input 1404 and / or any other input based on the input 1404.

[0822] In some exemplary embodiments, the NN-based estimator 1410 may apply any suitable linear and / or non-linear estimation functions to the input 1408. For example, the estimation function implemented by the NN-based estimator 1410 may include a non-linear estimation function, such as a radial basis function. In other embodiments, any other additional or alternative suitable estimation functions may be implemented by the NN-based estimator 1410.

[0823] In some exemplary embodiments, the NN-based estimator 1410 may be configured to generate a sound control signal 1412 based on one or more inputs of acoustic sensor information 1416 corresponding to, for example, one or more acoustic sensing positions as described above.

[0824] In some exemplary embodiments, one or more inputs of the acoustic sensor information 1416 may include residual noise sensor information corresponding to, for example, one or more residual noise sensing positions, such as one or more residual noise sensing positions 107 (FIG. 2) as described above.

[0825] In some exemplary embodiments, one or more inputs of the acoustic sensor information 1416 may include monitoring sensor information corresponding to, for example, one or more residual monitoring positions, such as one or more monitoring positions 102 (FIG. 2) as described above.

[0826] In some exemplary embodiments, one or more inputs of the acoustic sensor information 1416 may include information from an acoustic sensor at the acoustic sensing position as described above.

[0827] In some exemplary embodiments, one or more inputs of the acoustic sensor information 1416 may include virtual sensor information corresponding to a virtual acoustic sensor at a virtual acoustic sensing position as described above.

[0828] In some exemplary aspects, one or more inputs of the acoustic sensor information 1416 may include at least one virtual microphone input corresponding to residual noise (“noise error”) sensed by at least one virtual error sensor at at least one specific residual noise sensor location of the location 107 (FIG. 2). For example, the controller 1400 may evaluate the noise error at a specific residual noise sensor location based on, for example, the input 1408 and the predicted noise signal 1412, as will be described later.

[0829] In some exemplary aspects, the NN-based estimator 1410 may include a multiple-input multiple-output (MIMO) prediction unit configured to generate a plurality of sound control patterns corresponding to the nth sample, including, for example, M control patterns represented by y1(n) ··· y M (n), for driving each of the plurality of M acoustic transducers 1414 based on, for example, the input 1408.

[0830] In some exemplary aspects, as shown in FIG. 14A, the controller 1400 may include an AFB reducer component (“echo canceller”) 1418 configured to partially or entirely reduce, remove, and / or cancel a part of the signal generated by the acoustic transducer 1414 from the input signal 1404.

[0831] In one example, the AFB reducer 1418 may be configured to reduce the AFB between the acoustic transducer 1414, such as one or more acoustic transducers 108 (FIG. 1), and one or more noise inputs 1402, such as the noise input 106 (FIG. 1).

[0832] In some exemplary embodiments, the AFB reducer 1418 may include, or be based on, an acoustic transducer signal 1412 provided to the acoustic transducer 1414, for example, as described hereinafter, and may be configured to generate an AFB reduction signal 1459 by applying an AFB setting 1451 to the acoustic transducer signal 1457.

[0833] In some exemplary embodiments, the AFB setting 1451 may include a plurality of AFB reduction coefficients applied to the acoustic transducer signal 1457 to generate, for example, the AFB reduction signal 1459. For example, the AFB reducer 1418 may be configured to filter the acoustic transducer signal 1457 according to the AFB setting 1451, for example, based on the product of the acoustic transducer signal 1457 and the AFB reduction coefficient.

[0834] In some exemplary embodiments, the controller 1400 may be configured to determine an AFB-reduced signal, for example, by subtracting the AFB reduction signal 1459 from the input signal 1404.

[0835] In some exemplary embodiments, the AFB reducer 1418 may include an AFB reduction NN 1455 that may be trained to generate the AFB setting 1451, for example, based on the AAC information 1429. For example, the AFB reduction NN 155 (FIG. 1) may include the AFB reduction NN 1455 and / or may perform one or more operations and / or functions of the AFB reduction NN 1455.

[0836] Refer to FIG. 14B, which schematically shows a training scheme 1470 for training the AFB reduction NN 1471 according to some exemplary embodiments.

[0837] For example, the AFB reduction NN 1455 (FIG. 14A) may include one or more elements of the AFB reduction NN 1471 and / or may perform one or more operations and / or functions of the AFB reduction NN 1471.

[0838] In some example aspects, the AFB reduction NN 1471 can be trained to generate an AFB reduction setting 1473 for AFB reduction between an acoustic transducer 1478 and an acoustic sensing location 1477, such as an error sensing location, a monitoring sensing location, and / or a noise sensing location.

[0839] In some example aspects, the AFB reduction NN 1471 can be trained to generate the AFB setting 1473 based on, for example, AAC information 1499. For example, the AAC information 1499 may include AAC information 129 (FIG. 1) from an information source 120 (FIG. 1).

[0840] In some example aspects, as shown in FIG. 14B, the AFB reduction NN 1471 can be trained based on an acoustic signal 1475 sensed at the acoustic sensing location 1477. In one example, the signal 1475 can be sensed by an acoustic sensor 1491 at the acoustic sensing location 1477.

[0841] In one example, the acoustic sensor 1491 may include a noise sensor, such as a reference noise sensor 119 (FIG. 1).

[0842] In another example, the acoustic sensor 1491 may include a residual noise sensor, such as a residual noise sensor 121 (FIG. 1).

[0843] In some example aspects, the signal 1475 may be based on, for example, as described below, the difference between a first signal 1481 and a second signal 1483.

[0844] In some example aspects, the first signal 1481 may be applied by the acoustic transducer 1478 and may be based on a sound control pattern 1485 from a noise generator 1489 that can be received by the acoustic sensor 1491 at the acoustic sensing location 1477, for example, via an actual TTF (STF) between the acoustic transducer 1478 and the acoustic sensing location 1477.

[0845] In some example aspects, the second signal 1483 can be generated by applying, for example, an AFB setting 1473 generated by an AFB reduction NN 1471 to a sound control pattern 1485 from a noise generator 1489.

[0846] In some example aspects, for example, during training of the AFB reduction NN 1471, a predetermined noise pattern 1485 can be generated by the noise generator 1489, a setting of predetermined AAC information 1499 can be provided as an NN input to the AFB reduction NN 1471, and a signal 1475 can be determined.

[0847] In some example aspects, for example, during training of the AFB reduction NN 1471, the determination of the perceived acoustic signal 1475 can be repeated for, for example, a plurality of different predetermined noise patterns 1485 and / or a plurality of different settings of predetermined AAC information 1499.

[0848] In some example aspects, for example, during training of the AFB reduction NN 1471, the AFB reduction NN 1471 can be trained based on a training criterion for minimizing or removing, for example, the perceived acoustic signal 1475.

[0849] Now, refer to FIG. 15 schematically showing a MIMO prediction unit 1500 according to some example aspects. In some example aspects, the NN-based estimator 1410 (FIG. 14A) can include the MIMO prediction unit 1500 and / or can perform one or more functions and / or operations of the MIMO prediction unit 1500.

[0850] In some example aspects, as shown in FIG. 15, the prediction unit 1500 can be configured to receive AAC information 1529 including, for example, AAC information 129 (FIG. 1).

[0851] In some exemplary aspects, as shown in FIG. 15, the prediction unit 1500 may be configured to receive an input 1512 that can be based on the noise input 1402 (FIG. 14A). For example, the input 1512 may be based on the output from the extractor 1406 (FIG. 14A), as described above.

[0852] In some exemplary aspects, as shown in FIG. 15, the prediction unit 1500 may be configured to drive a loudspeaker array 1502 including M transducers, such as the acoustic transducer 108 (FIG. 2). For example, the prediction unit 1500 may drive the plurality of M respective acoustic transducers, such as the acoustic transducer 108 (FIG. 2), based on, for example, the input 1408 (FIG. 14A), to generate a controller output 1501 including M sound control patterns y1(n) ··· y M (n).

[0853] In some exemplary aspects, interference (crosstalk) between two or more of the M acoustic transducers of the array 1502 may occur, for example, when two or more of the M acoustic transducers, such as some or all, generate a control noise pattern, for example, simultaneously.

[0854] In some exemplary aspects, the prediction unit 1500 may generate an output 1501 configured to control the array 1502 to generate a substantially optimal sound control pattern while simultaneously optimizing the input signals to each speaker within the array 1502. For example, the prediction unit 1500 may control the multi-channel speakers of the array 1502 while canceling interference between the speakers.

[0855] Refer to FIG. 16 which schematically shows an implementation of the controller 1600 in an AAC system according to some exemplary aspects. For example, the controller 193 (FIG. 1), the controller 1400 (FIG. 14A), and / or the NN-based AAC system 400 (FIG. 4) may include one or more elements of the controller 1600 (FIG. 16) and / or may perform one or more operations and / or functions of the controller 1600.

[0856] In some exemplary aspects, the controller 1600 may be configured to receive an input 1612 including residual noise from a plurality of microphones (RMIC) and generate an output signal 1601 for driving a speaker array 1602 including M acoustic transducers, for example, three speakers or any other number of speakers. For example, the input 1612 may include the input 106 (FIG. 1), the input 1416 (FIG. 14A), and / or the input 404 (FIG. 4).

[0857] In some exemplary aspects, as shown in FIG. 16, the controller 1600 may include a plurality of prediction filters indicated by PF along a path between the input 1612 and the output signal 1601.

[0858] In some exemplary aspects, the controller 1600 may include a PF NN, for example, PF NN152 (FIG. 1) and / or PF NN452 (FIG. 4), which may be configured to configure, determine, update, and / or set one or more parameters of the PF on one or more paths between the input 1612 and the output signal 1601, for example, based on the AAC information 129 (FIG. 1) as described above.

[0859] In some exemplary aspects, the controller 1600 may be configured to configure, determine, update, and / or set one or more parameters of an AFB setting, such as an EC setting, applied to one or more paths between the output signal 1601 and the input 1612, based on, for example, the AAC information 129 (FIG. 1) as described above. The controller 1600 may include an AFB NN, such as the AFB NN 155 (FIG. 1) and / or the AFB NN 1455 (FIG. 14A).

[0860] Referring again to FIG. 1, in some exemplary aspects, the AAC controller 193 may be configured according to a hybrid PF scheme, as described hereinafter, for example.

[0861] In some exemplary 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, as described hereinafter, for example.

[0862] In some exemplary aspects, the NN-based noise prediction filter may be configured to be applied to a prediction filter input that may be based on the noise input 104, as described hereinafter, for example.

[0863] In some exemplary aspects, the NN-based residual noise prediction filter may be configured to be applied to a prediction filter input that may be based on acoustic sensor information at one or more acoustic sensing locations. For example, the acoustic sensor information at one or more acoustic sensing locations may include, for example, the residual noise input 106 at the residual noise sensing location 107 and / or the monitoring input at the monitoring location 103, as described above.

[0864] Referring now to FIG. 17, which schematically shows a controller 1700 according to some exemplary aspects. For example, controller 193 (FIG. 1) may include one or more elements of controller 1700 and / or may perform one or more operations and / or functions of controller 1700.

[0865] In some exemplary aspects, controller 1700 may be configured according to a hybrid PF scheme.

[0866] In some exemplary aspects, as shown in FIG. 17, controller 1700 may include at least one of, for example, an NN-based estimator 1710 and / or an NN-based estimator 1720, as will be described later.

[0867] In some exemplary aspects, as shown in FIG. 17, the NN-based estimator 1710 may include a noise NN-based estimator that is applied to an NN-based estimator input 1712, which may be based on a noise input 1706 from, for example, one or more noise sensors 1718 (“reference microphones”). For example, the NN-based estimator input 1712 may be based on the noise input 104 (FIG. 1).

[0868] In some exemplary aspects, the NN-based estimator 1720 may include a residual noise NN-based estimator applied to the NN-based estimator input 1722 that may be based on the 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, for example, as described above, residual noise sensed by one or more residual noise sensors 1728 (“error microphones”) at one or more residual noise sensing locations. For example, the NN-based estimator input 1722 may be based on the residual noise input 106 (FIG. 1). For example, the NN-based estimator 1710 and / or the NN-based estimator 1720 may be configured to perform one or more operations and / or functions of an NN-based PF, such as the NN-based PF 152, as described above.

[0869] In some exemplary aspects, the 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, the controller 1700 may evaluate the noise error at the virtual sensing location based on the input 1726 and the predicted noise signal 1729.

[0870] In some exemplary aspects, as shown in FIG. 17, the controller 1700 may generate a sound control signal 1729 based on the outputs of the prediction unit 1710 and the NN-based estimator 1720, and may output the sound control signal 1729 to the acoustic transducer 1708.

[0871] In some exemplary aspects, the controller 1700 may generate a sound control signal 1729 configured to reduce and / or remove the noise energy and / or wave amplitude of one or more sound patterns within the sound control zone, although the noise energy and / or wave amplitude of one or more other sound patterns may not be affected within the sound control zone, as described later, for example.

[0872] In some example aspects, as shown in FIG. 17, the controller 1700 may include an extractor 1714 for extracting a plurality of reference acoustic patterns, such as non-identical reference acoustic patterns, from the input 1716. According to these aspects, the NN-based estimator input 1712 may include a plurality of reference acoustic patterns, such as non-identical reference acoustic patterns. In other aspects, the extractor 1714 may be excluded, 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.

[0873] In some example aspects, for example, as shown in FIG. 17, the controller 1700 may include an extractor 1724 for extracting a plurality of residual noise acoustic patterns, such as non-identical residual noise acoustic patterns, from the input 1726. According to these aspects, the NN-based estimator input 1722 may include a plurality of residual noise acoustic patterns, such as non-identical residual noise acoustic patterns. In other aspects, the extractor 1724 may be excluded, and the NN-based estimator input 1722 may be generated directly or indirectly based on the input 1726, for example, according to any other algorithm and / or calculation.

[0874] In some example aspects, as shown in FIG. 17, the controller 1700 may include an echo processing component (an “echo canceller”) 1715 calibrated to at least partially reduce, remove, and / or cancel 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.

[0875] In some example aspects, the echo canceller 1715 may include an AFB reduction NN, such as AFB NN 155 (FIG. 1), that may be trained to generate EC coefficients for the echo canceller 1715, for example, based on the AAC information 1732, as described above.

[0876] In some exemplary aspects, as shown in FIG. 17, the controller 1700 may include an echo processing component (an “echo canceller”) 1725 configured to at least partially or entirely reduce, remove, and / or cancel a part of the signal generated by the speaker 1708 from the acoustic information 1726, for example, in the output signal of the residual noise microphone 1728, as described above.

[0877] In some exemplary aspects, the echo canceller 1725 may include an AFB reduction NN, such as the AFB NN 155 (FIG. 1), trained to generate EC coefficients for the echo canceller 1725, for example, based on the AAC information 1732, as described above.

[0878] In some exemplary aspects, the controller 1700 may be configured to determine, update, and / or adjust, for example, in real time, the setting of at least one acoustic pattern extractor parameter of the extractor 1714 and / or the extractor 1724 based on, for example, the AAC information 1732. For example, the extractor 1714 may be configured to determine a plurality of reference acoustic patterns, such as non-identical reference acoustic patterns, for the input 1712 based on the acoustic pattern extractor parameter setting based on the AAC information 1732. For example, the extractor 1724 may be configured to determine a plurality of residual noise acoustic patterns, such as non-identical residual noise acoustic patterns, for the input 1722 based on the acoustic pattern extractor parameter setting based on the AAC information 1732.

[0879] Referring to FIG. 18, which schematically shows a vehicle 1800 including an AAC system according to some exemplary aspects.

[0880] In one example, the vehicle 1840 may include one or more elements and / or components of the AAC system 100 (FIG. 1) configured to control the sound in one or more sound control zones within the vehicle 1800, for example.

[0881] In some exemplary aspects, as shown in FIG. 18, vehicle 1800 may include a plurality of speakers 1808, a plurality of reference sensors (“ambient microphones”) 1810, and a plurality of residual noise sensors (“monitoring microphones”) 1812.

[0882] In some exemplary aspects, vehicle 1800 may include an AAC controller 102 (FIG. 1) configured to control the plurality of speakers 1808 to provide a first sound control zone 1830 for the driver of vehicle 1800, for example, at the position of the headrest of the driver's seat.

[0883] In some exemplary aspects, the AAC controller 102 (FIG. 1) may be configured to control the plurality of speakers 1808 to provide a second sound control zone 1826 for a passenger, for example, at the position of the headrest of the passenger seat, for example, in the front seat near the driver's seat.

[0884] In some exemplary aspects, as shown in FIG. 18, the plurality of monitoring microphones 1812 may be located within the first and / or second sound control zones 1830 and 1826.

[0885] In some exemplary aspects, as shown in FIG. 18, the plurality of ambient microphones 1810 may be located in an environment outside the sound control zones 1830 and 1826.

[0886] In other aspects, vehicle 1800 may include any other number of the plurality of speakers 1808, the plurality of monitoring microphones 1812, and / or the plurality of ambient microphones 1810, any other arrangement, position, and / or location of the plurality of speakers 1808, the plurality of monitoring microphones 1812, and / or the plurality of ambient microphones 1810, and / or any other additional or alternative components.

[0887] Refer to FIG. 19 showing the NN-based AAC method. For example, one or more of the operations in FIG. 19 may be performed by one or more components of an AAC system, such as AAC system 100 (FIG. 1), AA system 400 (FIG. 4), AAC system 700 (FIG. 7), AAC system 1000 (FIG. 10), and / or AAC system 1200 (FIG. 12), a controller, such as controller 102 (FIG. 1), controller 193 (FIG. 1), controller and / or 1400 (FIG. 14A), MIMO prediction unit 1500 (FIG. 15), controller 1600 (FIG. 16), and / or controller 1700 (FIG. 7).

[0888] In some exemplary aspects, as shown in 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 positions, for example, within a vehicle. For example, controller 193 (FIG. 1) may be configured to process input information 195 (FIG. 1) including noise input 104 (FIG. 1) and / or AAC input 129 (FIG. 1), as described above.

[0889] In some exemplary aspects, as shown in block 1904, the method may include determining a sound control pattern for controlling sound within a sound control zone based on the plurality of noise inputs. For example, the sound control pattern may be based on the NN output of an NN trained to generate an NN output based on the NN input. For example, the NN input may be based on the AAC configuration information. For example, controller 193 (FIG. 1) may be configured to determine the sound control pattern based on the NN output of NN 150 (FIG. 1) trained to generate an NN output based on an NN input including, for example, AAC configuration information 129 (FIG. 1), thereby determining the sound control pattern based on the plurality of noise inputs 104 (FIG. 1), as described above.

[0890] In some exemplary aspects, as shown at block 1906, the method may include outputting a sound control pattern to one or more acoustic transducers. For example, the controller 193 (FIG. 1) may be configured to output a sound control signal 109 (FIG. 1) for controlling the acoustic transducer 108 (FIG. 1) to generate a sound control pattern, as described above.

[0891] Refer to FIG. 20, which schematically shows a product 2000 according to some exemplary embodiments. When executed by at least one processor, such as a computer processor, the product 2000 enables at least one processor to perform one or more operations of one or more elements of the AAC system 100 (FIG. 1), the controller 102 (FIG. 1), the controller 193 (FIG. 1), the NN 150 (FIG. 1), the AAC system 400 (FIG. 4), the AAC system 700 (FIG. 7), the AAC system 1000 (FIG. 10), the AAC system 1200 (FIG. 12), the controller 1400 (FIG. 14A), the MIMO prediction unit 1500 (FIG. 15), and / or the controller 1700 (FIG. 7), causes one or more elements of the AAC system 100 (FIG. 1), the controller 102 (FIG. 1), the controller 193 (FIG. 1), the NN 150 (FIG. 1), the AAC system 400 (FIG. 4), the AAC system 700 (FIG. 7), the AAC system 1000 (FIG. 10), the AAC system 1200 (FIG. 12), the controller 1400 (FIG. 14A), the MIMO prediction unit 1500 (FIG. 15), and / or the controller 1700 (FIG. 7) to execute, trigger, and / or control one or more operations, and / or perform, trigger, and / or implement one or more operations, communications, and / or functions described above with reference to FIGS. 1-19 and / or one or more operations described herein, and may include one or more tangible computer-readable ( "machine-readable") non-transitory storage media 2002 implemented, for example, by logic 2004. The phrases "non-transitory machine-readable media" and "computer-readable non-transitory storage media" can be directed to include all machine and / or computer-readable media, with the sole exception being transient propagation signals.

[0892] In some example aspects, product 2000 and / or machine-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 rewritable memory, and the like. For example, machine-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 memory), content addressable memory (CAM), polymer memory, phase change memory, ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, disks, hard drives, and the like. A computer-readable storage medium may include any suitable medium involved in carrying 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 propagated medium via a communication link, such as a modem, wireless, or network connection.

[0893] In some example aspects, logic 2004 may include instructions, data, and / or code that, when 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, and the like.

[0894] In some exemplary aspects, the logic 2004 may include, or be implemented as, software, software modules, applications, programs, subroutines, instructions, instruction sets, computing code, words, values, symbols, etc. The instructions may include any suitable type of code, such as, for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. The instructions may be implemented according to a predetermined computer language, scheme, or syntax for instructing the processor to perform a particular function. The instructions may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled, and / or interpreted programming language, machine code, etc.

Example

[0895] The following examples relate to further aspects.

[0896] Example 1 includes an input for receiving input information comprising 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, the controller being configured to process the input information to determine a sound control pattern for controlling sound within a sound control zone based on the plurality of noise inputs, and comprising a neural network (NN) trained to generate an NN output based on an NN input based on the AAC configuration information, the controller being configured to generate a sound control pattern based on the NN output, and an output for outputting the sound control pattern to one or more acoustic transducers.

[0897] Example 2 includes the subject matter of Example 1, the NN comprising a predictive filter (PF) NN trained to generate an NN output comprising a PF setting based on the NN input, the controller being configured to generate a sound control pattern by applying a PF configured according to the PF setting to noise information, the noise information being based on the plurality of noise inputs.

[0898] Example 3 includes the subject matter of Example 2 and, optionally, the PF setting comprises a plurality of PF coefficients.

[0899] Example 4 includes the subject matter of Example 2 or 3 and, optionally, the controller comprises a parameter extractor configured to determine the extracted AAC parameter information based on sensor information from one or more sensors, and the NN input comprises the extracted AAC parameter information.

[0900] Example 5 includes the subject matter of Example 4 and, optionally, the parameter extractor comprises a principal component analysis (PCA) extractor configured to determine the extracted AAC parameter information based on PCA of the sensor information.

[0901] Example 6 includes the subject matter of Example 4 or 5 and, optionally, 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.

[0902] Example 7 includes the subject matter of any one of Examples 4 to 6 and, optionally, the extracted AAC parameter information comprises at least one of road type information, cabin state information, passenger position information, vehicle system state information, noise characteristic information, or noise state information, the road type information comprises information corresponding to the type of road on which a vehicle equipped with a sound control zone travels, the cabin state information comprises information corresponding to the state of the cabin of the vehicle, the passenger position information comprises information corresponding to the position of one or more passengers in the cabin of the vehicle, the vehicle system information comprises information corresponding to the state of the system of the vehicle, the noise characteristic information comprises information representing one or more characteristics of the noise to be reduced, and the noise state information comprises information corresponding to the state of the noise to be reduced.

[0903] Example 8 includes one of the subject matters of Examples 2 to 7, and optionally, the controller is configured to train the PF NN based on noise errors at one or more error sensing positions.

[0904] Example 9 includes one of the subject matters of any one of Examples 2 to 8, and optionally, the controller is configured to configure the PF NN based on the transducer transfer function (TTF) setting of the TTF between one or more acoustic transducers and one or more acoustic sensing positions.

[0905] Example 10 includes the subject matter of Example 9, and one or more acoustic sensing positions include at least one of a residual noise sensing position within a sound control zone, a monitoring sensing position, or a noise sensing position outside the sound control zone.

[0906] Example 11 includes one of the subject matters of any one of Examples 2 to 10, and optionally, the controller includes a transducer transfer function (TTF) NN trained to determine the TTF setting based on the AAC configuration information, the TTF setting includes the setting of the TTF between one or more acoustic transducers and one or more acoustic sensing positions, and the controller is configured to configure the PF NN based on the TTF setting.

[0907] Example 12 includes the subject matter of Example 11, and optionally, one or more acoustic sensing positions include at least one of a residual noise sensing position within a sound control zone, a monitoring sensing position outside the sound control zone, or a noise sensing position outside the sound control zone.

[0908] Example 13 includes the subject matter of Example 11 or 12, and optionally, the controller is configured to train the TTF NN based on the sensed acoustic information corresponding to one or more acoustic sensing positions.

[0909] Example 14 includes the subject matter of any one of Examples 2 to 13. Optionally, the input information includes noise error information representing noise errors at one or more error sensing positions, and the controller is configured to determine noise information based on the noise error information.

[0910] Example 15 includes the subject matter of any one of Examples 1 to 14. Optionally, the controller includes a parameter extractor configured to determine the extracted AAC parameter information based on the AAC configuration information, and the NN input includes the extracted AAC parameter information.

[0911] Example 16 includes the subject matter of any one of Examples 1 to 15. Optionally, the NN input includes noise error information representing noise errors at one or more error sensing positions.

[0912] Example 17 includes the subject matter of any one of Examples 1 to 16. Optionally, the input information includes noise error information representing noise errors at one or more error sensing positions, and the NN input is based on the noise error information.

[0913] Example 18 includes the subject matter of any one of Examples 1 to 17. Optionally, the NN includes an acoustic feedback (AFB) reducer NN trained to generate an NN output with an AFB setting based on the NN input, and the controller is configured to determine an AFB reduction signal by applying the AFB setting to an acoustic transducer signal according to a sound control pattern, and the controller determines the sound control pattern based on the AFB reduction signal.

[0914] Example 19 includes the subject matter of Example 1. Optionally, the NN is trained to generate an NN output based on an NN input including the AAC configuration information and noise information based on a plurality of noise inputs.

[0915] Example 20 includes any one of the subject matters of Examples 1 to 19. Optionally, the AAC configuration information includes information corresponding to the configuration of AAC within the sound control zone.

[0916] Example 21 includes any one of the subject matters of Examples 1 to 20. Optionally, the AAC configuration information includes information representing the spectral distribution of the acoustic signal in at least one of the sound control zone or the environment of the sound control zone.

[0917] Example 22 includes any one of the subject matters of Examples 1 to 21. Optionally, the AAC configuration information includes information representing one or more parameters that affect the real-time configuration of AAC within the sound control zone.

[0918] Example 23 includes any one of the subject matters of Examples 1 to 22. Optionally, the AAC configuration information includes information representing one or more physical characteristics of the sound control zone.

[0919] Example 24 includes any one of the subject matters of Examples 1 to 23. Optionally, the AAC configuration information includes information representing one or more acoustic characteristics of the sound control zone.

[0920] Example 25 includes any one of the subject matters of Examples 1 to 24. Optionally, the plurality of noise inputs are based on the noise sensed by one or more acoustic sensors, and the AAC configuration information includes information from one or more information sources different from the one or more acoustic sensors.

[0921] Example 26 includes any one of the subject matters of Examples 1 to 25. Optionally, the plurality of noise inputs are based on the noise sensed by one or more acoustic sensors, and the AAC configuration information includes information from one or more information sources independent of the one or more acoustic sensors.

[0922] Example 27 includes any one of the subject matters of Examples 1 to 26. Optionally, the AAC configuration information includes vehicle speed information corresponding to the speed of a vehicle having a sound control zone.

[0923] Example 28 includes any one of the subject matters of Examples 1 to 27. Optionally, the AAC configuration information includes engine information corresponding to the engine of a vehicle having a sound control zone.

[0924] Example 29 includes any one of the subject matters of Examples 1 to 28. Optionally, the AAC configuration information includes at least one of brake system information, road detection information, steering information, tire information, seat information, vehicle type information, or opening state information. The brake system information includes information corresponding to the brake system of a vehicle having a sound control zone. 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 information includes information corresponding to at least one of 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. The opening state information includes information corresponding to the state of the opening of the vehicle.

[0925] Example 30 includes any one of the subject matters of Examples 1 to 29. Optionally, the AAC configuration information includes passenger information corresponding to one or more passengers of a vehicle having a sound control zone.

[0926] Example 31 includes any one of the subject matters of Examples 1 to 30. Optionally, the AAC configuration information includes audio system information corresponding to the audio system of a vehicle having a sound control zone.

[0927] Example 32 includes any one of the subject matters of Examples 1 to 31. Optionally, the AAC configuration information includes climate information corresponding to at least one of the climate inside the sound control zone or the climate outside the sound control zone.

[0928] Example 33 includes the subject matter of any one of Examples 1 to 32. Optionally, the AAC configuration information includes user position information corresponding to at least one position of the user's head or ear within the sound control zone.

[0929] Example 34 includes the subject matter of any one of Examples 1 to 33. Optionally, the AAC configuration information includes user identity information corresponding to the user's identity to control the user's preference for the sound control zone.

[0930] Example 35 includes the subject matter of any one of Examples 1 to 34. Optionally, the AAC configuration information includes vehicle system configuration information corresponding to the configuration of the operating mode of one or more vehicle systems of a vehicle having a sound control zone.

[0931] Example 36 includes the subject matter of any one of Examples 1 to 35. Optionally, the AAC configuration information includes vehicle sensor information from one or more vehicle sensors of a vehicle having a sound control zone.

[0932] Example 37 includes the subject matter of any one of Examples 1 to 36. Optionally, NN is trained to generate an NN output having settings of one or more AAC parameters, and the controller is configured to generate a sound control pattern based on the settings of the one or more AAC parameters.

[0933] Example 38 includes the subject matter of Example 37, and the settings of the one or more AAC parameters include at least one setting of a prediction filter or a transfer function.

[0934] Example 39 includes the subject matter of any one of Examples 1 to 38. Optionally, the input is configured to receive AAC configuration information via a system bus of a vehicle having a sound control zone.

[0935] Example 40 includes the subject matter of Example 39, and optionally, the input is configured to receive AAC configuration information via at least one of controller area network (CAN) bus information received via a vehicle's CAN bus, A to B (A2B) bus information received via a vehicle's A2B bus, media oriented system transport (MOST) bus information received via a vehicle's MOST bus, wireless communication information received via a wireless communication link, or Ethernet bus information received via a vehicle's Ethernet bus.

[0936] Example 41 includes the subject matter of any one of Examples 1 to 40, and optionally, NN comprises a deep neural network (DNN).

[0937] Example 42 includes 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 positions, and a controller for processing input information comprising AAC configuration information and the plurality of noise inputs, which determines a sound control pattern for controlling sound within a sound control zone based on the plurality of noise inputs, and comprises a neural network (NN) trained to generate an NN output based on an NN input based on the AAC configuration information, and is configured to generate a sound control pattern based on the NN output, and an active acoustic control (AAC) system.

[0938] Example 43 includes the AAC system of Example 42, and optionally, includes any of the apparatuses of Examples 1 to 41.

[0939] Example 44 includes a vehicle including a plurality of seats and any of the systems of Examples 42 to 43.

[0940] Example 45 includes a product comprising one or more tangible computer-readable non-transitory storage media that, when executed by at least one processor, are operable to include instructions that enable the at least one processor to cause an active acoustic control (AAC) system to control sound within a sound control zone. The instructions, when executed, cause the AAC system to process input information including active acoustic control (AAC) configuration information and a plurality of noise inputs representative of acoustic noise at a plurality of noise sensing locations, determine a sound control pattern for controlling sound within the sound control zone based on the plurality of noise inputs, where the sound control pattern is based on an NN output of an NN trained to generate an NN output based on an NN input based on the AAC configuration information, and output the sound control pattern to one or more acoustic transducers.

[0941] Example 46 includes the subject matter of Example 45, and optionally, the processor is configured to cause the AAC system to perform one or more operations according to any of Examples 1-41.

[0942] Example 47 includes a system comprising an apparatus according to any of Examples 1-41.

[0943] Example 48 includes an apparatus comprising means for performing any of the operations described in Examples 1-41.

[0944] Example 49 includes an apparatus comprising a memory interface and a processing circuit configured to perform any of the operations described in Examples 1-41.

[0945] Example 50 includes a method comprising any of the operations described in Examples 1-41.

[0946] The functions, operations, components, and / or features described herein with reference to one or more aspects may be combined with, or utilized in combination with, one or more other functions, operations, components, and / or features described herein with reference to one or more other aspects, and vice versa.

[0947] Although specific features have been illustrated and described herein, those skilled in the art may envision numerous modifications, alternatives, variations, and equivalents. Accordingly, it should be understood that the appended claims are intended to embrace all such modifications and variations that fall within the true spirit of the present disclosure.

Claims

1. An active acoustic control (AAC) device, the device comprising a controller equipped with a neural network (NN), the controller, The process involves receiving input information, wherein the input information includes AAC configuration information and a plurality of noise inputs representing acoustic noise at a plurality of noise detection locations. The process involves processing the input information to determine a sound control pattern for controlling sound within a sound control zone based on the plurality of noise inputs, wherein the controller is configured to generate the sound control pattern based on the NN output of the NN, and the NN is based on the AAC configuration information. A device configured to perform the following actions.

2. The apparatus according to claim 1, wherein the NN comprises a PFNN trained to produce an NN output having a predictive filter (PF) setting based on the NN input, and the controller is configured to generate the sound control pattern by applying the PF configured according to the PF setting to noise information, the noise information being based on the plurality of noise inputs.

3. The apparatus according to 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, and the NN input comprises the extracted AAC parameter information.

4. The apparatus according to claim 3, wherein the parameter extractor comprises a PCA extractor configured to determine the extracted AAC parameter information based on principal component analysis (PCA) of the sensor information.

5. The apparatus according to 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, and 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 state information, passenger location information, vehicle system state information, noise characteristic information, and noise state information, the road type information comprises information corresponding to the type of road on which the vehicle equipped with the sound control zone travels, the cabin state information comprises information corresponding to the state of the vehicle cabin, the passenger location information comprises information corresponding to the position of one or more passengers in the vehicle cabin, the vehicle system state information comprises information corresponding to the state of the vehicle system, the noise characteristic information comprises information representing one or more characteristics of the noise to be reduced, and the noise state information comprises information corresponding to the state of the noise to be reduced.

7. The apparatus according to claim 2, wherein the controller is configured to train the PFNN based on noise errors at one or more error sensing locations.

8. The apparatus according to claim 2, wherein the controller configures the PFNN based on the TTF setting of the transducer transfer function (TTF) between one or more acoustic transducers and one or more acoustic sensing positions.

9. The apparatus according to claim 2, wherein the controller comprises a TTFNN trained to determine transducer transfer function (TTF) settings based on the AAC configuration information, the TTF settings comprising TTF settings between one or more acoustic transducers and one or more acoustic sensing positions, and the controller configures the PFNN based on the TTF settings.

10. The apparatus according to claim 9, wherein the controller is configured to train the TTFNN based on sensed acoustic information corresponding to one or more acoustic sensing positions.

11. The apparatus according to claim 2, wherein the input information comprises noise error information representing noise errors at one or more error detection locations, and the controller is configured to determine the noise information based on the noise error information.

12. The apparatus according to claim 1, wherein the controller comprises a parameter extractor configured to determine extracted AAC parameter information based on the AAC configuration information, and the NN input comprises the extracted AAC parameter information.

13. The apparatus according to claim 1, wherein the NN input comprises noise error information representing noise errors at one or more error sensing locations.

14. The apparatus according to claim 1, wherein the NN comprises an AFB reducer NN trained to produce an NN output having an acoustic feedback (AFB) setting based on the NN input, the controller is configured to determine an AFB reducer signal by applying the AFB setting to an acoustic transducer signal according to a sound control pattern, and the controller determines the sound control pattern based on the AFB reducer signal.

15. The apparatus according to claim 1, wherein the NN is trained to generate the NN output based on an 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 includes information representing the spectral distribution of an acoustic signal in at least one of the sound control zone and the environment of the sound control zone.

17. The apparatus according to any one of claims 1 to 15, wherein the AAC configuration information comprises information representing one or more parameters that affect the real-time configuration of the AAC in the sound control zone.

18. The apparatus according to any one of claims 1 to 15, wherein the AAC configuration information comprises 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 comprises 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 produce an NN output having one or more AAC parameter settings, and the controller is configured to produce the sound control pattern based on the one or more AAC parameter settings.

21. The apparatus according to any one of claims 1 to 15, wherein the input is configured to receive the AAC configuration information via the system bus of a vehicle equipped with the sound control zone.

22. An active acoustic control (AAC) system comprising the apparatus described in any one of claims 1 to 15, wherein the AAC system is One or more acoustic transducers, Multiple noise-sensing acoustic sensors for generating the aforementioned multiple noise inputs, Equipped with, The controller is configured to provide the sound control pattern to one or more acoustic transducers, in an AAC system.

23. A method for active acoustic control (AAC), wherein the method comprises: Processing AAC configuration information and input information comprising multiple noise inputs representing acoustic noise at multiple noise detection locations, The process involves determining a sound control pattern for controlling sound within a sound control zone based on the aforementioned plurality of noise inputs, wherein the sound control pattern is based on the NN output of a neural network (NN) trained to generate an NN output based on the NN input, and the NN input is based on the AAC configuration information. Outputting the aforementioned sound control pattern for one or more acoustic transducers, A method that includes [a certain feature].

24. The method according to claim 23, wherein the NN comprises a PFNN trained to produce an NN output having a predictive filter (PF) setting based on the NN input, and determining the sound control pattern comprises determining the sound control pattern by applying the PF configured according to the PF setting to noise information, the noise information is based on the plurality of noise inputs.

25. The method according to claim 23, wherein the NN input comprises noise error information representing noise errors at one or more error sensing locations.

26. The method according to claim 23, wherein the NN is trained to generate the NN output based on an NN input comprising the AAC configuration information and noise information based on the plurality of noise inputs.

27. ​​The method according to claim 23, wherein the AAC configuration information comprises information representing the spectral distribution of acoustic signals in the sound control zone and / or the environment of the sound control zone, information representing one or more parameters that affect the real-time configuration of the AAC in the sound control zone, information representing one or more physical characteristics of the sound control zone, and / or information representing one or more acoustic characteristics of the sound control zone.

28. A product comprising one or more tangible computer-readable non-temporary storage media having instructions that, when executed by at least one processor, enable the at least one processor to cause an active acoustic control (AAC) controller to perform the method according to any one of claims 23 to 27.

29. An apparatus comprising means for performing the method described in any one of claims 23 to 27.

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