Computational architecture for active noise reduction device

Abstract

To provide a computational architecture for efficiently processing a different ANR processing function.SOLUTION: A personal active noise reduction (ANR) device 10 includes: a communication interface 12 that receives an audio stream 32, a driver 26, a microphone system 28 from a DSP system 14, and an outgoing audio stream 36; a first DSP 20 that receives a signal from the audio stream and the microphone system, performs ANR on the audio stream according to a set of parameters to be implemented, and outputs the processed audio stream 34; a second DSP 22 that generates state data in response to analysis of the audio stream 32, and analysis of a signal from the microphone system and the processed audio stream and changes an operation parameter on the first DSP; and a general purpose processor 24 that communicates a control signal 40 with a communication interface, processes the state data from the second DSP, and changes a parameter on the first DSP.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] (Priority Claim) This application claims priority to U.S. Patent Application No. 16 / 788,365, filed February 12, 2020, which is incorporated herein by reference in its entirety.

[0002] FIELD OF THE INVENTION The present disclosure relates generally to personal active noise reduction (ANR) devices, and more particularly to a computational architecture for efficiently handling different ANR processing functions. [Background technology]

[0003] Headphones and other physical configurations of personal ANR devices worn around a user's ears have become commonplace, with the goal of isolating the user's ears from unwanted environmental sounds. ANR headphones combat unwanted environmental noise through the active generation of anti-noise signals. These ANR headphones contrast with passive noise reduction (PNR) headsets, which merely physically isolate the user's ears from environmental noise. Of particular interest to users are ANR headphones that incorporate audio listening capabilities, allowing users to listen to electronically provided audio (e.g., playback of recorded audio or audio received from another device) without introducing unwanted environmental noise.

[0004] As ANR devices become more prevalent, the demand for increased performance and more robust features drives the need for more complex computational requirements. For example, in addition to providing state-of-the-art signal processing, ANR devices are tasked with providing enhanced features, such as multiple I / O ports (e.g., Bluetooth, USB, etc.), high-quality telephone service, noise level control management, event processing, user experience command processing, etc. Along with the increased computational requirements, more complex hardware is added to ANR devices, increasing both cost and power consumption. Summary of the Invention [Means for solving the problem]

[0005] All examples and features mentioned below can be combined in any technically possible manner.

[0006] A system and method are disclosed that describes a computational architecture for efficiently handling different ANR processing functions of an ANR device.

[0007] In some implementations, the described computing architecture includes at least three separate processors, each configured to perform a set of computing functions appropriate to the individual processor. In such cases, the architecture allows various types of required functions to be handled by a processor that matches the requirements of the task (e.g., priority, speed, memory resources). By dividing the functions among different processors, computational efficiency is gained and power consumption is reduced.

[0008] One aspect provides a personal active noise reduction (ANR) device, the ANR device including a communication interface configured to receive a source audio stream and a control signal, a driver, a microphone system, and an ANR computational architecture.

[0009] In a particular implementation, the ANR computation architecture includes: a first DSP processor configured to receive a source audio stream and a signal from a microphone system, the first DSP processor performing ANR on the source audio stream according to a set of operating parameters introduced in the first DSP processor and outputting a processed audio stream to a driver; a second DSP processor generating state data in response to analysis of at least one of the source audio stream, the signal from the microphone system, and the processed audio stream and modifying the set of operating parameters on the first DSP processor; and a general-purpose processor operably coupled to the first DSP processor and the second DSP processor, the general-purpose processor configured to communicate control signals with a communication interface, process the state data from the second DSP processor, and modify the set of operating parameters on the first DSP processor.

[0010] Implementations may include one or any combination of the following features.

[0011] In certain aspects, the operating parameters are selected from the group consisting of filter coefficients, compressor settings, signal mixers, gain conditions, and signal routing options.

[0012] In another aspect, the status data generated by the second DSP processor includes error conditions detected in the processed audio stream.

[0013] In a further aspect, the condition data generated by the second DSP processor includes frequency domain overload conditions detected in the processed audio stream.

[0014] In some implementations, the state data generated by the second DSP includes sound pressure level (SPL) information detected from the microphone system and the processed audio stream.

[0015] In a further implementation, the communication interface includes a Bluetooth system.

[0016] In certain cases, the general-purpose processor includes a sleep mode for conserving power, the sleep mode being configured to be activated by at least one of the first DSP processor, the second DSP processor, and the communication interface.

[0017] In certain aspects, the general-purpose processor is further configured to apply machine learning to the state data received from the second DSP processor.

[0018] In certain implementations, the general-purpose processor is further configured to apply machine learning to the time-based signal, which in some cases includes blocks of raw audio data received from a microphone system and / or via a Bluetooth system.

[0019] In another aspect, the operating parameters include filter coefficients, and the general-purpose processor is further configured to calculate and install updated filter coefficients in the first DSP processor.

[0020] In some cases, the general-purpose processor is further configured to evaluate the status data to identify an impairment status of the personal ANR.

[0021] Two or more of the features described in this disclosure, including the features described in this Summary section, may be combined to form implementations not specifically described herein.

[0022] The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. [Brief explanation of the drawings]

[0023] [Figure 1]FIG. 1 is a block diagram of an ANR device having a hierarchical computing architecture according to various implementations. [Figure 2] 1 shows a detailed diagram of a computing architecture according to various implementations. [Figure 3] 1 illustrates an exemplary personal ANR wearable according to various implementations.

[0024] It should be noted that the drawings of the various implementations are not necessarily to scale. The drawings are intended to depict only typical aspects of the disclosure and therefore should not be considered limiting of the scope of the implementations. In the drawings, like numbering represents like elements between the drawings. DETAILED DESCRIPTION OF THE INVENTION

[0025] Various implementations of the present disclosure describe a computational architecture for an active noise reduction (ANR) device that includes at least three separate processors, each configured to perform a set of computational functions appropriate for the individual processor. Thus, the architecture allows each required function to be processed by a processor that matches the task's requirements (e.g., priority, speed, memory resources). By dividing the functions among different processors, computational efficiency can be gained and power consumption can be reduced.

[0026] While this disclosure provides architectures for devices such as headphones that employ ANR, an exhaustive description of ANR is omitted for brevity. Where necessary, exemplary ANR systems are described, for example, in U.S. Patent No. 8,280,066, entitled "Binaural Feedforward-based ANR," issued October 2, 2012 to Joho et al., and U.S. Patent No. 8,184,822, entitled "ANR Signal Processing Topology," issued May 22, 2012 to Carreras et al., the contents of both of which are incorporated herein by reference.

[0027] The solutions disclosed herein are intended to be applicable to a wide variety of personal ANR devices, i.e., devices structured to be at least partially worn by a user near at least one of the user's ears and provide ANR functionality for at least one ear. Various specific implementations of personal ANR devices can include headphones, two-way communication headsets, earphones, earbuds, audio glasses, wireless headsets (also known as "earsets"), and ear protectors, although it should be noted that the presentation of specific implementations is intended to facilitate understanding through use of the examples and should not be construed as limiting the scope of the present disclosure or the scope of the claims.

[0028] Furthermore, the solutions disclosed herein are applicable to personal ANR devices that provide two-way voice communication, one-way voice communication (i.e., an acoustic output of voice provided electronically by another device), or no communication. Furthermore, what is disclosed herein is applicable to personal ANR devices that connect to other devices wirelessly, via an electrically and / or optically conductive cable, or that are not connected to any other device. These teachings are applicable to personal ANR devices having a physical structure configured to be worn near either one or both of a user's ears, including, but not limited to, headphones with one or two earpieces, overhead headphones, behind-the-neck headphones, headsets with a communication microphone (e.g., a boom microphone), wireless headsets (i.e., headsets), audio glasses, a single earphone or a pair of earphones, as well as hats, helmets, clothing, or any physical structure incorporating one or two earpieces to enable voice communication and / or ear protection.

[0029] Beyond personal ANR devices, what is disclosed and claimed herein is also meant to be applicable to providing ANR in relatively small spaces where a person can sit or stand, including, but not limited to, telephone booths, automobile cabins, etc.

[0030] FIG. 1 is a block diagram of a personal ANR device 10, which in one embodiment may be configured to be worn by a user and provide active noise reduction (ANR) near at least one of the user's ears. The personal ANR device 10 may have any of several physical configurations, such as configurations incorporating a single earpiece to provide ANR to only one of the user's ears, other configurations incorporating a pair of earpieces to provide ANR to both of the user's ears, and other configurations incorporating one or more standalone speakers to provide ANR to the user's surrounding environment. Note, however, that for simplicity of explanation, only a single device 10 is shown and described in connection with FIG. 1. As will be described in further detail, the personal ANR device 10 incorporates functionality that may provide either or both feedback-based ANR and feedforward-based ANR, in addition to being able to further provide passing audio.

[0031] In the exemplary embodiment of FIG. 1 , ANR device 10 includes a wireless communication interface, in this case a Bluetooth system 12, that provides communication with an audio gateway device (or simply, gateway device) 30, such as a smartphone, wearable smart device, laptop, tablet, server, etc. Bluetooth system 12 may be implemented, for example, as a Bluetooth system-on-chip (SoC), a Bluetooth low energy (BLE) module, or in any other manner. Note that while ANR device 10 is shown as providing wireless communication using Bluetooth system 12, any type of wireless technology (e.g., Wi-Fi Direct, cellular, etc.) could instead be used. Communication with ANR device 10 may also occur via a first universal serial bus (USB) port 16 that interfaces with Bluetooth system 12 and / or a second USB port 18 that interfaces with general-purpose (GP) processor 24. GP processor 24 is one of at least three processors implemented in ANR device 10; the other processors are first digital signal processing (DSP) processor 20 and second DSP processor 22, which together form DSP system 14.

[0032] In a typical application, a source audio stream 32 is received from gateway device 30 via Bluetooth system 12 and sent to DSP system 14, where first DSP processor 20 performs ANR and generates a processed audio stream 34, which is then distributed via acoustic driver 26 (i.e., speaker). Microphone system 28 captures environmental noise sounds provided to DSP system 14 and provides, for example, a reference signal for generating noise-prevention sounds for ANR. For example, using the captured sounds, a noise-prevention signal is calculated and output by acoustic driver 26 with an amplitude and time shift calculated to acoustically interact with undesirable noise sounds in the surrounding environment. Microphone system 28 may also be used to capture a user's voice, such as in a telephony application, which can communicate via output audio stream 36 to Bluetooth system 12 and then to gateway device 30. It will be understood that the number and location of individual microphones in microphone system 28 will depend on the specific requirements of ANR device 10. Furthermore, as mentioned above, rather than using the Bluetooth system 12 to communicate with the gateway device 30, any type of communication interface may be implemented, such as USB ports 16, 18, or other communication ports and protocols (not shown).

[0033] In addition to audio streams, control signals 40 may also be communicated between gateway device 30 and GP processor 24. Control signals 40 may include, for example, data packets from gateway device 30 (e.g., to update a controllable noise cancellation (CNC) level), ANR device-generated data packets communicated to gateway device 30 (e.g., to provide coordination between a pair of earbuds), and user-generated control signals (e.g., to skip to the next song, answer a call, set a CNC level, etc.). Furthermore, as described in further detail herein, GP processor 24 can generate feedback 42 (e.g., product usage characteristics, fault detection, etc.) that can be reported to gateway device 30 and / or a remote service such as cloud platform 31. Feedback 42 may be used to enhance the user experience, for example, by providing details about how ANR device 10 is being used, reporting error conditions, etc.

[0034] ANR device 10 generally includes additional components, which are omitted for simplicity, including, for example, a power source, visual input / output such as a GUI and / or LED indicators, tactile input / output, power and control switches, additional memory, capacitive inputs, sensors, etc.

[0035] As previously mentioned, the computational architecture of ANR device 10 utilizes at least three separate processors that provide a modular, hierarchical operating platform for implementing the functions associated with ANR device 10. Using this architecture, the processing power of each processor is matched to a specific task to enhance system efficiency. In general, first DSP processor 20 provides a set of core ANR algorithms 50 designed to provide active noise reduction to audio stream 32. Second DSP processor 22 provides a set of signal analysis (SA) algorithms 52 designed to analyze ANR operation, provide status data such as operating characteristics, faults, etc., and automatically adjust parameters within ANR algorithms 50 in response to any available signals within ANR device 10. GP processor 24 provides a set of high-level functions 54, such as managing user controls, providing I / O processing, and handling events generated by DSP system 14.

[0036] FIG. 2 illustrates the processor hierarchy and characteristics in more detail. In this exemplary embodiment, both the first DSP processor 20 and the second DSP processor 22 share a common bus 21 to access the GP processor 24, microphone system 28, audio stream, etc. As described herein, the first DSP processor 20 includes a set of core ANR algorithms 50 that process the input audio stream 32 (FIG. 1), including, for example, feedback loop processing, compensation processing, feedforward loop processing, and audio equalization. The core ANR algorithms 50 may include operational ANR parameters that dictate, for example, filter coefficients, compressor settings, signal mixers, gain conditions, signal routing options, etc. The core ANR algorithms 50 are generally stream-processing oriented and may be characterized as processes that require high levels of processor performance but are relatively low in complexity. In particular, the functions performed by the core ANR algorithms 50 are intended to operate very quickly with minimal processing options and storage requirements. These types of stream processing functions require very low latency, for example, on the order of 1 to 10 microseconds. Furthermore, because the first DSP processor 20 provides the core ANR functionality, the first DSP processor 20 must be continuously powered as long as the ANR device 10 is operational. Therefore, the first DSP processor 20 is tuned to perform the calculations of the ANR algorithm 50 using as little power as possible.

[0037] The second DSP processor 22 does not directly provide ANR processing, but instead includes a set of signal analysis algorithms 52 that analyze signals and generate status data, for example, characterizing signals within the ANR device 10, the ANR processing performed by the first DSP processor 20. The status data may include, for example, fault information, instability detection, performance characteristics, error conditions, frequency-domain overload conditions, sound pressure level (SPL) information, etc. The signal analysis algorithms 52 perform different types of analysis, which may use thresholds and rules. For example, if a set of frequency characteristics deviates from an expected range, a fault may be triggered and a corresponding "event" may be output to the GP processor 24, which may then take corrective action.

[0038] Any process adapted to analyze the signal may be deployed in the second DSP processor 22 . Non-limiting exemplary signal analysis algorithms 52 are described, for example, in U.S. Pat. No. 10,244,306, issued March 26, 2019, entitled "Real-time detection of feedback instability" (e.g., describing instability detection), U.S. Patent Application Publication No. 2018 / 0286374, entitled "Parallel Compensation in Active Noise Reduction Devices," U.S. Patent Application Publication No. 2018 / 0286373, entitled "Active Noise Reduction Devices," U.S. Patent Application Publication No. 2018 / 0286375, entitled "Automatic Gain Control in Active Noise Reduction (ANR) Signal Flow Path" (e.g., describing overload situations), and U.S. Patent Application Publication No. 2019 / 0130928, entitled "Compressive Hear-through in Personal Acoustic Devices" (e.g., describing control of ANR to produce maximum sound volume at the ear), each of which is incorporated herein by reference in its entirety.

[0039] As described herein, the second DSP processor 22 can also directly modify the operating (i.e., ANR) parameters of the first DSP processor 20. For example, in certain cases, a signal analysis algorithm 52 is deployed to automatically adjust the ANR parameters (i.e., within the core ANR algorithm 50) to achieve a desired experience based on internal signals captured from the algorithms 50, 52, from the GP processor 24, from any of the microphones 28, from the input audio stream 32, and / or from the control signal 40. For example, in certain implementations, the ANR parameters are adjusted using external signals monitored by the algorithm 52, such as external sound pressure level (SPL) characteristics received by the microphone(s) 28.

[0040] Because the second DSP processor 22 does not directly implement core ANR services, it requires a relatively small amount of performance but provides a relatively large amount of computational complexity. For example, in certain cases, the tasks performed by the second DSP processor 22 may tolerate longer latencies, e.g., approximately 100 microseconds to 10 milliseconds. Like the first DSP processor 20, the second DSP processor is continuously powered during operation of the device 10. In certain implementations, the second DSP processor 22 is configured to perform both stream and block processing and includes a moderate amount of data storage and programmability to efficiently perform analytical tasks.

[0041] The GP processor 24 includes a set of high-level functions 54 that are one level removed from the ANR processing performed by the first DSP processor 20. The specific functions 54 performed by the GP processor 24 may depend on the requirements of the ANR device 10. An exemplary set of functions is shown in FIG. 2. In a particular exemplary implementation, a communications algorithm 56 handles I / O and command processing functions. In some cases, the communications algorithm 56 includes a unified messaging interface for translating different communications protocols (e.g., USB vs. Bluetooth) into a common protocol. The unified messaging interface allows commands for interpretation to be stored and implemented in a single location (i.e., the GP processor 24), thus allowing all commands to be routed to the GP processor 24 for processing.

[0042] GP processor 24 is generally tasked with handling more numerous and more complex calculations. In some implementations, GP processor 24 calculates “one-time” filter coefficients customized for each individual user based on how the product fits on the head. In particular implementations, user experience algorithms 64 analyze the user's fit based on, for example, control signals 40 and feedback 42, and communication algorithms 56 notify the user to adjust the fit of device 10 in response to the fitting algorithms.

[0043] In various implementations, the GP processor 24 further includes an ANR control algorithm 58 that updates operating parameters of the first DSP processor 20 in response to events received from the DSP system 14 or in response to control signals 40 received from the gateway device 30 (FIG. 1). In some cases, the control algorithm 58 implements CNC (controllable noise cancellation) features, etc.

[0044] As previously mentioned, GP processor 24 may receive "events" from second DSP processor 22 indicating instability or some other problem, detected using, for example, the techniques described in U.S. Pat. No. 10,244,306 (previously incorporated by reference herein). If an immediate change is required to mitigate the instability based on one or more received events, second DSP processor 22 is typically responsible for modifying the ANR parameters in first DSP processor 20. Regardless of whether an immediate change is required, GP processor 24 may record the event(s) generated in local memory and report the event(s) via Bluetooth system 12 (FIG. 1).

[0045] After collecting a series of events, the GP processor 24 can utilize one or more of its algorithms to identify and / or address the situation. For example, if multiple instability events are detected, the system health algorithm 62 is deployed to determine whether a more severe problem exists (e.g., a malfunction of the ANR device 10). If a malfunction is identified, the system health algorithm 62 is configured to characterize the malfunction, and based on the nature of the malfunction, the system health algorithm 62 directly initiates an ANR parameter change in the DSP processor 20. In other cases, the system health algorithm 62 performs other operations, such as analyzing the event data, reporting the analysis to the gateway device 30, or applying machine learning to determine the cause of the malfunction. As described, the damage status of the ANR device 10 is reported to the gateway device 30 to notify the device user (or another user) that the ANR device 10 is malfunctioning.

[0046] By way of example, if an instability event is detected, e.g., using techniques described in U.S. Pat. No. 10,244,306 (previously incorporated by reference), GP processor 24 records the event. If the number of detected instability events exceeds a predetermined threshold, GP processor 24 is configured to provide notification (e.g., to the device user or another user) that device 10 is likely malfunctioning. Similarly, if measured data indicates an anomaly (e.g., poor fit characterized by an unexpected difference in feedback versus feedforward microphone signals) when calculating filter coefficients customized for an individual user based on how the product fits on the user's head, GP processor 24 provides feedback instructing the user to adjust the device, e.g., for fit.

[0047] In other cases, tuning algorithm 60 is implemented to tune performance between a pair of earphones (e.g., earbuds, over-ear audio devices, etc.). For example, in response to detecting that a first earphone is operating at a low ANR performance level (e.g., due to a detected impairment), tuning algorithm 60 matches the ANR performance level of the first earphone to that of the second earphone to avoid performance mismatches and ensure a better user experience.

[0048] In various embodiments, user experience algorithms 64 are implemented to provide user controls such as volume, equalization, and to implement different modes of operation such as phone calls, music listening, etc. User experience algorithms 64 can be implemented to analyze sensor data to automatically control ANR device 10 (e.g., provide special settings when on an airplane), collect and provide feedback that can be analyzed remotely, etc. In other cases, algorithms 64 respond to status data that ANR device 10 is not a good fit (e.g., does not detect a proper seal with the user's ear canal) and output a warning (e.g., to the device user or another user).

[0049] In additional implementations, GP processor 24 is configured to implement a machine learning model or event classifier. In some embodiments, GP processor 24 is configured to apply machine learning to state data received from second DSP processor 22. In more particular embodiments, GP processor 24 is configured to apply machine learning to state data received from second DSP processor 22 and to time-based signals, such as blocks of raw audio data. In some cases, the time-based signals (which may include raw or unprocessed audio data) are received via microphone system 28 and / or Bluetooth system 12 (e.g., as audio stream 32). Exemplary machine learning techniques involving signal processing are described in U.S. patent application Ser. No. 16 / 425,550, filed May 29, 2019, entitled "Automatic Active Noise Reduction (ANR) Control," and U.S. patent application Ser. No. 16 / 690,675, filed November 21, 2019, entitled "Active Transit Vehicle Classification," both of which are incorporated herein by reference in their entireties.

[0050] In further implementations, a lightweight operating system (OS) and / or function library 66 may be implemented to instantiate various functions 54 on the GP processor 24, allowing algorithms and routines to be easily accessed, added, and removed, allowing software updates to be performed, providing access to storage, providing for the use of higher level scripting and / or programming languages, and the like.

[0051] Because the GP processor 24 does not perform any time-critical signal processing services, the GP processor 24 can be implemented with relatively low performance but requires a relatively large amount of computational complexity to provide a wide range of functionality. Latency can be relatively high, for example, on the order of 100 milliseconds to 10 seconds, when performing a function. Furthermore, because its functionality is not always needed, the GP processor 24 is configured to be placed into a low-power or sleep mode when not needed (e.g., when no event is detected or analysis is required). The sleep mode is configured to be activated by at least one of control signals received from the first DSP processor 20, the second DSP processor 22, and / or one of the communication interfaces. Generally, the GP processor 24 does not need to handle any stream processing and processes data as blocks using standard memory configurations. Data storage can be implemented, for example, using internal storage and / or a flash drive, as needed.

[0052] FIG. 3 is a schematic diagram of an exemplary wearable audio device 70 including the ANR device 10 of FIG. 1. In this example, the wearable audio device 70 is an audio headset including two earphones (e.g., in-ear headphones, also known as "earbuds") 72, 74. While the earphones 72, 74 are shown in a "true" wireless configuration (i.e., no tethering between the earphones 72, 74), in additional implementations, the audio headset 70 includes a tethered wireless configuration (whereby the earphones 72, 74 are connected to a playback device via a telephone line over a wireless connection) or a wired configuration (whereby at least one of the earphones 72, 74 has a wired connection to a playback device). Each illustrated earphone 72, 74 includes a body 76 that may include a casing formed of one or more plastic or composite materials. The body 76 may include a nozzle 78 for insertion into a user's ear canal entrance and a support member 80 for holding the nozzle 78 in a resting position within the user's ear. Each earphone 72, 74 includes an ANR device 10 for performing some or all of the various functions described herein. Other wearable device forms can similarly be implemented using the analog device 10, such as around-ear headphones, audio glasses, open-ear audio devices, etc.

[0053] It is understood that one or more of the functions of ANR device 10 may be implemented as hardware and / or software, and that the various components may include communication paths connecting the components by any conventional means (e.g., wired and / or wireless connections). For example, one or more non-volatile devices (e.g., centralized or distributed devices such as flash memory device(s)) may store and / or execute programs, algorithms, and / or parameters of one or more systems (e.g., Bluetooth system 12, DSP system 14, GP 24, etc.) within ANR device 10. Additionally, the functionality described herein, or portions thereof, and various modifications thereof (hereinafter "functions"), may be implemented, at least in part, via a computer program product (e.g., a computer program tangibly embodied in an information carrier, such as one or more non-transitory machine-readable media, for execution by or to control the operation of one or more data processing devices (e.g., programmable processors, computers, multiple computers, and / or programmable logic components, etc.)).

[0054] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer, on multiple computers at one site, or distributed across multiple sites and interconnected by a network.

[0055] Actions associated with performing all or a portion of the functions may be performed by one or more programmable processors executing one or more computer programs to perform the functions. All or a portion of the functions may be implemented as special purpose logic circuitry, such as an FPGA (field programmable gate array) and / or an ASIC (application-specific integrated circuit). Processors suitable for executing computer programs also include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor may receive instructions and data from a read-only memory, a random-access memory, or both. Elements of a computer include a processor for executing instructions and one or more memory devices for storing instructions and data.

[0056] Additionally, actions associated with implementing all or a portion of the functionality described herein may be performed by one or more networked computing devices that may be connected via a network, e.g., one or more wired and / or wireless networks, such as a local area network (LAN), a wide area network (WAN), a personal area network (PAN), an internet-connected device and / or network, and / or cloud-based computing (e.g., cloud-based servers).

[0057] In various implementations, electronic components described as "coupled" may be linked via conventional wired and / or wireless means such that the electronic components can communicate data with one another. Furthermore, subcomponents within a given component may be considered to be linked via conventional paths, although not necessarily shown.

[0058] Although multiple implementations have been described, it is nevertheless understood that additional modifications may be made without departing from the scope of the inventive concepts described herein, and, accordingly, other implementations are within the scope of the following claims. [Explanation of symbols]

[0059] 10 ANR devices 12 Bluetooth System 14 DSP Systems 16 USB ports 18 USB ports 20 First DSP Processor 21 Common Bus 22 Second DSP Processor 24 General Purpose (GP) Processors 26 Acoustic Drivers 28 microphones 30 Gateway Devices 31 Cloud Platform 32 audio streams 34 processed audio streams 36 output audio streams 40 Control Signal 42 Feedback 50 ANR algorithm 52 SA Algorithm 54 Functions 56 Communication Algorithms 58 ANR Control Algorithm 60 Adjustment Algorithm 62 System Health Algorithm 64 User Experience Algorithms 66 Lightweight OS and libraries 70 Wearable Audio Devices 72 Earphones 74 Earphones 76 Main Unit 78 nozzles 80 Support member

Claims

1. 1. A personal active noise reduction (ANR) device comprising: a communications interface configured to receive the source audio stream and control signals; Driver and A microphone system; 1. An ANR computation architecture, comprising: a first DSP processor configured to receive the source audio stream and a signal from the microphone system, the first DSP processor performing ANR on the source audio stream according to a set of operating parameters installed in the first DSP processor, and outputting a processed audio stream to the driver; a second DSP processor configured to generate state data and modify the set of operating parameters on the first DSP processor in response to analysis of at least one of the source audio stream, the signal from the microphone system, and the processed audio stream; and an ANR computation architecture comprising: a general-purpose processor operatively coupled to the first DSP processor and the second DSP processor, the general-purpose processor configured to communicate control signals with the communication interface, process status data from the second DSP processor, and modify the set of operating parameters on the first DSP processor; A personal ANR device comprising:

2. 10. The personal ANR device of claim 1, wherein the operating parameters are selected from the group consisting of filter coefficients, compressor settings, signal mixers, gain conditions, and signal routing options.

3. 2. The personal ANR device of claim 1, wherein the status data generated by the second DSP processor includes error conditions detected in the processed audio stream.

4. 10. The personal ANR device of claim 1, wherein the condition data generated by the second DSP processor includes frequency domain overload conditions detected in the processed audio stream.

5. 10. The personal ANR device of claim 1, wherein the status data generated by the second DSP processor includes sound pressure level (SPL) information detected from the microphone system and processed audio stream.

6. 2. The personal ANR device of claim 1, wherein the general-purpose processor includes a sleep mode for conserving power, the sleep mode being configured to be activated by at least one of the first DSP processor, the second DSP processor, and the communication interface.

7. The personal ANR device of claim 1 , wherein the general-purpose processor is further configured to apply machine learning to the state data received from the second DSP processor.

8. The personal ANR device of claim 7 , wherein the general-purpose processor is further configured to apply machine learning to time-based signals.

9. 10. The personal ANR device of claim 1, wherein the operating parameters include filter coefficients, and the general-purpose processor is further configured to calculate and install updated filter coefficients in the first DSP processor.

10. The personal ANR device of claim 1 , wherein the general-purpose processor is further configured to evaluate the condition data to identify a damage condition of the personal ANR.

11. 1. An active noise reduction (ANR) computing architecture, comprising: a first DSP processor configured to receive a source audio stream, perform ANR on the source audio stream according to a set of operating parameters installed in the first DSP processor, and output a processed audio stream; a second DSP processor configured to generate state data and modify the set of operating parameters within the first DSP in response to analysis of at least one of the source audio stream, microphone input, and the processed audio stream; a general-purpose processor operatively coupled to the first DSP processor and the second DSP processor, the general-purpose processor configured to communicate control signals with a communications interface, process status data from the second DSP processor, and modify the set of operating parameters within the first DSP processor; An ANR computation architecture comprising:

12. The ANR computation architecture of claim 11 , wherein the operating parameters are selected from the group consisting of filter coefficients, compressor settings, signal mixers, gain conditions, and signal routing options.

13. 12. The ANR computation architecture of claim 11, wherein the status data generated by the second DSP processor includes error conditions detected in the microphone input and processed audio stream.

14. 12. The ANR computation architecture of claim 11, wherein the condition data generated by the second DSP processor includes frequency domain overload conditions detected in the processed audio stream.

15. 12. The ANR computation architecture of claim 11, wherein the state data generated by the second DSP processor includes sound pressure level (SPL) information detected from the processed audio stream.

16. 12. The ANR computing architecture of claim 11, wherein the general-purpose processor includes a sleep mode for conserving power, the sleep mode being configured to be invoked by at least one of the first DSP processor, the second DSP processor, and the communication interface.

17. The ANR computing architecture of claim 11 , wherein the general-purpose processor is further configured to apply machine learning to the state data received from the second DSP processor.

18. The ANR computation architecture of claim 17 , wherein the general-purpose processor is further configured to apply machine learning to time-based signals.

19. The ANR computation architecture of claim 11 , wherein the general-purpose processor is further configured to compute and install updated filter coefficients in the first DSP processor.

20. The ANR computing architecture of claim 11 , wherein the general-purpose processor is further configured to evaluate condition data to identify a damage condition and communicate the damage condition to an external device via the communication interface.

Images