Patent Description:
Wearable audio devices, such as headphones, earphones and other physical configurations, are utilized for various purposes. In some instances, wearable audio devices include one or more technologies for eliminating unwanted noise. For example, many wearable audio devices include active noise reduction (ANR) for countering unwanted environmental noise with the generation of anti-noise signals. However, noise reduction systems can require significant processing resources and can consume significant power resources.

<CIT> discloses an earpiece with microphones, which sends a voice signal to a separate processing unit for performing blind source separation. The separated speech signal is communicated back to the earpiece over a wireless link.

<CIT> discloses a hearing aid and a source separation unit connected over a wireless link.

<CIT> discloses a headset and a source separation unit connected over a wireless link.

<NPL> discloses using machine learning (LSTM) for spectral mask estimation, the mask used in a beamformer.

<NPL> discloses generating a spectral mask with machine learning (LSTM) to be used in source separation.

<NPL> discloses generating spectral mask with a neural network for using in beamforming.

The present invention relates to a system for processing audio signals according to claim <NUM>. Advantageous embodiments are set forth in dependent claims of the appended claim set.

All examples and features mentioned below can be combined in any technically possible way.

Systems are disclosed that enable noise reduction in playback at audio devices. Some implementations include systems for reducing noise to improve intelligibility of a targeted audio signal, such as speech. In particular implementations, a system for processing audio signals includes: a wearable audio device having a transducer and a communication system; and an accessory device configured to wirelessly communicate with the wearable audio device, the accessory device having a processor configured to process a source audio signal according to a method that includes: separating the source audio signal into its constituent components (for example noisy speech separates to speech and noise); and providing the separated audio signals of interest (for example only speech) to the wearable audio device for transduction.

In additional particular implementations, a system for processing audio signals includes: a wearable audio device including a communication system, a transducer and a first processor, where the first processor includes an onboard machine learning model configured to process a source audio signal and generate a first source separated audio signal; an accessory device configured to wirelessly communicate with the wearable audio device, where the accessory device comprises a second processor having a remote machine learning module configured to process the source audio signal and generate a second source separated signal; and a supervisory process configured to selectively output the first source separated audio signal or the second source separated audio signal to the transducer.

In further particular implementations, a system for processing audio signals includes: an accessory device that includes a first processor for running a machine learning model on an input signal, where the machine learning model comprises a classifier configured to generate metadata associated with the input signal; and a wearable audio device configured for wireless communication with the accessory device, where the wearable audio device comprises a second processor that utilizes the metadata from the accessory device to process a source audio signal and output a processed audio signal.

Implementations may include one of the following features, or any combination thereof.

Source separating the source audio signal includes: converting the source audio signal into a first sequence of frequency transformed frames, each having a first frame size; converting the source audio signal into a second sequence of frequency transformed frames, each having a second frame size greater than the first frame size; applying a machine learning model to the second sequence of frequency transformed frames to create a spectral mask; applying the spectral mask to the first sequence of frequency transformed frames to generate a source separated sequence of frequency transformed frames of the first frame size; converting the source separated sequence of frequency transformed frames to a source separated time domain signal; where providing the source separated audio signal to the wearable audio device includes wirelessly transmitting the source separated time domain signal to the wearable audio device.

In certain aspects, converting the source audio signal into the first sequence of frequency transformed frames includes applying a window function to the source audio signal to generate frames of the first frame size and then applying a fast Fourier transform (FFT) to the frames.

In particular implementations, converting the source audio signal into the second sequence of frequency transformed frames includes applying a window function to the source audio signal to generate frames of the second frame size and then applying a fast Fourier transform (FFT) to the frames.

In some cases, converting the source audio signal into the second sequence of frequency transformed frames further includes: taking an absolute value of data in each frame after the FFT is applied; applying a spectral transform (for example a Mel filterbank) to the data in each frame.

In certain aspects, creating the spectral mask further includes performing an inverse spectral transform after the machine learning model is applied.

In some implementations, applying the spectral mask to the first sequence of frequency transformed frames includes performing a pointwise multiply operation, and converting the source separated sequence of frequency transformed frames to the source separated time domain signal includes: performing an inverse FFT on an output of the pointwise multiply operation; and performing an inverse window function on an output of the inverse FFT.

In particular cases, the remote machine learning model operates with a higher latency than the onboard machine learning model.

In certain aspects, the supervisory process utilizes the first source separated audio signal in response to a determination that the wearable audio device cannot communicate with the accessory device.

In some implementations, the supervisory process utilizes the second source separated audio signal in response to a determination that the earpiece has a low battery condition.

In particular aspects, both the wearable audio device and accessory device have a microphone configured to capture the source audio signal.

In certain cases, the system further includes a second supervisory process that combines the remote machine learning model and the onboard machine learning model to form an ensemble model, where an output of the remote machine learning model is passed as input to the onboard machine learning model.

In some implementations, at least one of the onboard machine learning model and remote machine learning model performs beamforming.

In certain aspects, the metadata identifies an environment within which the source audio signal is captured.

In particular cases, the environment includes at least one of: an airplane, an outdoor space, an indoor space, a vehicle, a quiet space, or an entertainment venue.

In some aspects, the metadata includes an identity of a person speaking to a user of the wearable audio device.

In certain implementations, the metadata determines whether the person speaking is a user of the wearable audio device.

In particular aspects, the wearable audio device includes a selectable set of machine learning models that are selected in response to the metadata generated by the accessory device.

Two or more features described in this disclosure, including those described in this summary section, may be combined to form implementations not specifically described herein.

It is 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 as limiting the scope of the implementations.

Various implementations describe distributed machine learning-based systems that include a wearable audio device and an accessory device. In certain implementations, the distributed machine learning-based systems source separate audio sources, which may include noise reduction, de-noising, separating audio information, etc., to enhance the performance or intended function of the wearable device. In particular implementations, the distributed machine learning-based systems enable efficient data processing, power management and/or form factors for wearable audio devices.

The solutions disclosed herein are intended to be applicable to a wide variety of wearable audio devices, i.e., devices that are structured to be at least partly worn by a user in the vicinity of at least one of the user's ears to provide audio for at least that one ear. It should be noted that although various specific implementations of wearable audio devices may include headphones, two-way communications headsets, earphones, earbuds, hearing aids, audio eyeglasses, wireless headsets (also known as "earsets') and ear protectors, presentation of specific implementations are intended to facilitate understanding through the use of examples, and should not be taken as limiting either the scope of disclosure or the scope of claim coverage.

Additionally, the solutions disclosed herein are applicable to wearable audio devices that provide two-way audio communications, one-way audio communications (i.e., acoustic output of audio electronically provided by another device), or no communications, at all. Further, what is disclosed herein is applicable to wearable audio devices that are wirelessly connected to other devices, that are connected to other devices through electrically and/or optically conductive cabling, or that are not connected to any other device, at all. These teachings are applicable to wearable audio devices having physical configurations structured to be worn in the vicinity of either one or both ears of a user, including and not limited to, headphones with either one or two earpieces, over-the-head headphones, behind-the neck headphones, headsets with communications microphones (e.g., boom microphones), in-the-ear or behind-the-ear hearing aids, wireless headsets (i.e., earsets), audio eyeglasses, single earphones or pairs of earphones, as well as hats, helmets, clothing or any other physical configuration incorporating one or two earpieces to enable audio communications and/or ear protection.

Beyond wearable audio devices, what is disclosed and claimed herein is also meant to be applicable to the provision of noise reduction in relatively small spaces in which a person may sit or stand, including and not limited to, phone booths, vehicle passenger cabins, etc..

In various implementations, the wearable audio devices described herein may incorporate active noise reduction (ANR) functionality that may include either or both feedback-based ANR and feedforward-based ANR, in addition to possibly further providing pass-through audio and audio processed through typical hearing aid signal processing such as dynamic range compression.

Additionally, the solutions disclosed herein are intended to be applicable to a wide variety of accessory devices, i.e., devices that can communicate with a wearable audio device and assist in the processing of audio signals. Illustrative accessory devices include smartphones, Internet of Things (IoT) devices, computing devices, specialized electronics, vehicles, computerized agents, carrying cases, charging cases, smart watches, other wearable devices, etc..

In various implementations, the wearable audio device and accessory device communicate wirelessly, e.g., using Bluetooth, or other wireless protocols. In certain implementations, the wearable audio device and accessory device reside within several meters of each other.

Various implementations detailed herein are described referencing de-noise systems and/or de-noised signals. However it is understood that the solutions provided herein can apply to any type of source separation system, including, e.g., noise reduction, separating audio, separating different speakers out of a mixture, separating sounds such as emergency vehicles, alarms, etc..

<FIG> is a block diagram of an illustrative distributed machine learning-based system <NUM> that includes an accessory device <NUM> and a wearable audio device <NUM>. As noted, the wearable audio device <NUM> may be structured to be worn by a user to provide an audio output to a vicinity of at least one of the user's ears. The wearable audio device <NUM> may have any of a number of form factors, including configurations that incorporate a single earpiece to provide audio to only one of the user's ears, others that incorporate a pair of earpieces to provide audio to both of the user's ears, and others that incorporate one or more standalone speakers to provide audio to the environment around the user. However, it should be noted that for the sake of simplicity of discussion, only a single device <NUM> is depicted and described in relation to the implementations described in <FIG>. Example wearable audio devices are illustrated and described in further detail in <CIT>), which is hereby incorporated by reference in its entirety.

In the illustrative implementation of <FIG>, accessory device <NUM> includes a processor <NUM> configured to process audio signals and a communication (comm. ) system <NUM> configured to communicate with the wearable audio device <NUM>. In this implementation, processor <NUM> processes audio signals from an audio source <NUM> captured via a microphone <NUM>. Audio source <NUM> may include any natural or manmade sounds (or, acoustic signals) and microphone <NUM> may include one or more microphones capable of capturing and converting the sounds into electronic signals.

In various implementations, processor <NUM> includes a de-noise system <NUM> that utilizes a remote machine learning (ML) model <NUM> to process audio signals and remove unwanted audio components (i.e., perform noise reduction). De-noise system <NUM> can be configured to provide various types of noise reduction using the remote ML model <NUM>. For example, remote ML model <NUM> can be trained to recognize and classify different types of acoustic signatures in sounds such as environmental sounds, a user's voice, other speakers, etc. Based on a recognized sound (e.g., acoustic signature), de-noise system <NUM> can perform an associated de-noise operation, such as utilizing a particular type of dynamic processing (e.g., via one or more filters or digital signal processing techniques) to cancel undesirable environmental sounds, provide beam-forming, remove the user's voice, remove the voices of other speakers, etc. In some cases, ML model <NUM> is configured to compute a set of filter parameters using a neural network in response to inputted signal data. Details of an illustrative ML process are described with reference to <FIG>.

In any case, once a de-noised signal <NUM> is generated by de-noise system <NUM>, the de-noised signal <NUM> is transmitted to a communication system <NUM> on the wearable device <NUM>. An audio processor <NUM> then outputs the de-noised signal <NUM> to an electrostatic transducer <NUM>, which outputs de-noised audio for the user. Using this approach, much of the computational complexity and power requirements associated with running the remote ML model <NUM> are off-loaded to the accessory device <NUM>, thus reducing the power consumption and computational resources of the wearable audio device <NUM>. Additionally, by reducing the resources required by the wearable audio device <NUM>, more flexibility is afforded to the design (i.e., form factor) of the wearable audio device <NUM>. For instance, the wearable audio device <NUM> can be made smaller since fewer hardware components are required.

<FIG> depicts a further implementation of an illustrative distributed machine learning-based system <NUM> that includes an accessory device <NUM> and a wearable audio device <NUM>. In illustrative implementations, accessory device <NUM> operates in a similar manner to the implementations of <FIG>. Namely, accessory device <NUM> includes a processor <NUM> having a de-noise system <NUM> with a remote machine learning (ML) model <NUM>. In response to capturing an audio signal from audio source (or sources) <NUM> via microphone <NUM>, processor <NUM> generates a de-noised signal <NUM> that is transmitted to the wearable device <NUM> via communication system <NUM>. Similar to the implementation of <FIG>, de-noise system <NUM> can apply any ML based process to classify and reduce noise in the captured audio signal (e.g., apply ANR, apply beamforming, recognize and filter out a voice, etc.).

In the illustrative implementation of <FIG>, wearable audio device <NUM> additionally includes a processor <NUM> having a de-noise system <NUM> with an onboard machine learning (ML) model <NUM>. Further, wearable audio device <NUM> includes a microphone (or set of microphones) <NUM> configured to capture an audio signal from the audio source (or sources) <NUM>. In response to the captured audio signal, de-noise system <NUM> generates a second de-noised signal <NUM>. Accordingly, wearable audio device <NUM> is configured to receive a first de-noised signal <NUM> from the accessory device <NUM> (via communication system <NUM>) and generate a second de-noised signal <NUM>. In various implementations, de-noised signals <NUM>, <NUM> are separately processed versions of the same audio source <NUM>. In other implementations, de-noised signals <NUM>, <NUM> originate from different or overlapping audio sources <NUM>, e.g., a first audio source primarily includes environmental background sounds while a second audio source primarily includes a person's voice.

In certain implementations, remote ML model <NUM> and onboard ML model <NUM> are configured to process captured audio signals differently. For example, accessory device <NUM> (such as a smartphone) may include more computing and power resources than a wearable audio device <NUM> (such as earbuds). Accordingly, remote ML model <NUM> may be configured to perform a more computationally intensive process than onboard ML model <NUM>. For example, remote ML model <NUM> may include a deep recurrent neural network such as a long short-term memory (LSTM) architecture to classify time-series audio data to learn multiple aspects of the user's environment, while onboard ML model <NUM> may be configured to perform more simple supervised learning, e.g., using Naive Bayes classification to identify a user's voice. Furthermore, in various implementations, the remote ML model <NUM> can operate with a higher latency than the onboard ML model <NUM>.

In certain implementations, the wearable audio device <NUM> includes a supervisor <NUM> to implement a supervisory process relating to the two de-noised signals <NUM>, <NUM>. For example, supervisor <NUM> can select which one of the two noise signals <NUM>, <NUM> to output to transducer <NUM>.

In particular cases, supervisor <NUM> selects the onboard generated de-noised signal <NUM> in response to determining that the wearable audio device <NUM> cannot communicate with the accessory device <NUM>. In these instances, the supervisor <NUM> is configured to periodically check with the communication system <NUM> to determine if a connection is established with the accessory device <NUM>, e.g., using a standard Bluetooth pairing and connection scheme.

In other cases, the supervisor <NUM> uses the remotely generated de-noised signal <NUM> in response to determining that the wearable audio device <NUM> is in a low power state. In these cases, the supervisor <NUM> checks the power state <NUM> of the wearable audio device <NUM>, e.g., via processor <NUM> or a separate connection with a battery (not shown). In response to determining that the wearable audio device <NUM> is in a low power state, the supervisor <NUM> selects the remotely generated de-noised signal <NUM> for output to the transducer <NUM>. Additionally, in response to determining that the wearable audio device <NUM> is in a low power state, either the supervisor <NUM> or the processor <NUM> can shut down the de-noise system <NUM> to conserve power.

In other cases, supervisor <NUM> is configured to evaluate both de-noised signals <NUM>, <NUM> to determine which of those signals <NUM>, <NUM> is of a higher quality. For instance, supervisor <NUM> can be configured to evaluate a speech modulation index. A greater modulation index can indicate cleaner speech since the presence of noise degrades the strength of modulation in a speech plus noise signal. In further implementations, both ML models <NUM>, <NUM> are configured to produce a confidence value associated with their respective outputs. In this case, supervisor <NUM> selects that de-noise signal with the highest confidence value.

It is appreciated that while a few examples have been provided herein relating to the selection process by supervisor <NUM>, any methodology can be utilized to make the selection. Further, while the supervisor <NUM> is shown embodied in the wearable audio device <NUM>, supervisor could reside elsewhere, e.g., on the accessory device <NUM>, on another device, in the cloud, etc..

In certain implementations, one or both devices <NUM>, <NUM> can include an input control (not shown) to allow a user to select between the two de-noised signals <NUM>, <NUM>. For example, at least one of the accessory device <NUM> or wearable audio device <NUM> includes a hardware or software switch that manually controls selection of one of the two de-noised signals <NUM>, <NUM>.

In further implementations, supervisor <NUM> is configured to combine the two de-noised signals <NUM>, <NUM> to output a single audio signal to the transducer <NUM>. For example, the results can be combined using a deep learning model. In this case, a signal to noise (SNR) estimate, e.g., from the speech modulation index noted above, could result in a "best band" merge of the two signals. The merge could be a machine learning based process or just a simple band selected based on an SNR metric. In other cases, one of the de-noised signals may reduce one type of noise while the other reduces another type of noise. For example, a simpler (e.g., on-bud) de-noising system <NUM> may be utilized to remove tonal sounds near the ear while a more powerful de-noising system <NUM> may be utilized to remove complex babble noise. Depending on prevalence of noise types detected, one of the two or a combination of both may be utilized.

<FIG> depicts a still further implementation of an illustrative distributed machine learning-based system <NUM> that includes an accessory device <NUM> and a wearable audio device <NUM>. In illustrative implementations, accessory device <NUM> operates in a similar manner to the implementations of <FIG> and <FIG>, except that rather than generating a de-noised signal, processor <NUM> includes a metadata system <NUM> that utilizes a remote machine learning (ML) model <NUM> to generate metadata <NUM>. In response to capturing an input signal from an audio source (or sources) <NUM> via microphone <NUM> (or via another type of sensor such as an accelerometer, optical sensor, thermometer, camera, etc.), processor <NUM> generates metadata <NUM> associated with the input signal. The metadata <NUM> is then transmitted to the wearable device <NUM> via communication system <NUM>.

Metadata system <NUM> can apply any ML based process to classify a captured input signal into metadata <NUM>. Illustrative metadata may include acoustic indicators or acoustic signatures about the ambient environment, including for example a characteristic of the environment (e.g., indoors versus outdoors, a social gathering, on a plane, in a quiet space, at an entertainment venue, in an auditorium, etc.), features of speech (e.g., the identity of the user, another person, a family member, etc., spectral energy, cepstral coefficients, etc.), features of noise (e.g., loud versus soft, high frequency versus low frequency, spectral energy, cepstral coefficients, etc.). Additionally, metadata <NUM> can include data accessible from another system at the accessory device <NUM>, e.g., location data about where the audio signals are captured, a time at which the audio signals are captured, etc..

In the illustrative implementation of <FIG>, wearable audio device <NUM> includes a processor <NUM> having a de-noise system <NUM> with an onboard ML model (or models) <NUM>. Similar to the implementation of <FIG>, wearable audio device <NUM> includes a microphone (or set of microphones) <NUM> configured to capture an audio signal from the audio source (or sources) <NUM>. In various approaches, wearable audio device <NUM> processes the captured audio signal with de-noise system <NUM> using an ML model <NUM>. In this implementation, de-noise system <NUM> utilizes the metadata <NUM> inputted via communication system <NUM> to process the audio signal and output a de-noised signal to transducer <NUM>.

In some implementations, the metadata <NUM> is used to select an ML model from a set of ML models <NUM>. For example, if the metadata <NUM> indicates that the user is in a social gathering with a room full of people, an ML model <NUM> may be selected that identifies friends and family of the user. De-noise system <NUM> can then filter out other noise not associated with the friends and family or amplify desired audio. In other implementations, the metadata <NUM> may be used to configure the onboard ML model <NUM> (e.g., set weights of nodes in a neural network) or specify filter parameters of the de-noise system <NUM>. Metadata <NUM> could also act on non-ML processing, such as the selection of a beamformer appropriate for a current environment, making a wind noise canceling algorithm more aggressive if wind is detected, etc..

In certain implementations, metadata may be bi-directional to allow both devices <NUM>, <NUM> to provide data to the other. For example, metadata <NUM> can be used to communicate information about the user's voice from processor <NUM> to processor <NUM>, which would be difficult for processor <NUM> to ascertain since processor <NUM> is closer to the user. Metadata <NUM> could then be used by processor <NUM> to facilitate generation of metadata <NUM>.

<FIG> depict an illustrative de-noise system <NUM> that employs an ML model <NUM>. In this example, an audio signal is presented as frames of a time domain waveform <NUM>, and is processed along two paths, a first (top) path A that converts the audio signal from time domain to frequency domain using a fast Fourier transform (FFT) <NUM>, and a second (bottom) path B that utilizes an ML model <NUM> to generate a mask (i.e., filter) that is applied to the signal at the end of the first path. The audio signal may, for example, originate from a mono input, a four-channel headphone array, or any other source.

Both paths A, B include a window function <NUM>, <NUM> that receives the time domain wave form <NUM> and converts the data to a predefined frame size, e.g., using a Hann or Gaussian function. The first (i.e., top path) window function <NUM> generates a window size that is relatively larger than that generated by the second (i.e., bottom path) window function <NUM>. For example, the top path window size may be on order of a <NUM>/<NUM> frame/hop size, while the bottom path window size may on the order of a <NUM>/<NUM> frame/hop size suitable for use with a short time Fourier transform (STFT). Note that smaller frame sizes can be processed faster than the larger frame size. In this manner, the processing of the actual audio signal on the top path A is faster than the calculation of the mask on the bottom path B. This allows for robust filter computations to be performed along the less critical bottom path B without delaying the actual audio signal on the top path A.

In various implementations, the bottom path B likewise performs an FFT <NUM> and then takes an absolute value <NUM>. This data is then processed by a spectral filter <NUM>, which converts the data into for example a Mel spectrogram. This data is then processed by the ML model <NUM>, which may, for example, include a deep neural network that outputs a latent spectral representation. This data is then processed by in inverse spectral transform <NUM> (such as an inverse Mel transform) to generate a real-valued spectral mask.

The spectral processing on the bottom path B allows the data to be processed using a short time Fourier transform (STFT). To sync with the delay caused by this processing, the top path A includes a delay <NUM> that allows the FFT signal data on the top path to be synched with the spectral mask data on the bottom path. The spectral mask is applied to the FFT signal data in a pointwise multiplication process <NUM>. An inverse FFT <NUM> is then applied to the masked signal, followed by an inverse window function <NUM> and an overlap add process <NUM>. The resulting de-noised signal is then output, for example at the transducer(s) on the wearable audio device (e.g., transducers <NUM>, <NUM>, <NUM>, in <FIG>).

<FIG> is a schematic depiction of an illustrative wearable audio device <NUM> that includes a de-noise system <NUM>, such as those described herein. In this example, the wearable audio device <NUM> is an audio headset that includes two earphones (for example, in-ear headphones, also called "earbuds") <NUM>, <NUM>. While the earphones <NUM>, <NUM> are shown in a "true" wireless configuration (i.e., without tethering between earphones), in additional implementations, the audio headset <NUM> includes a tethered wireless configuration (whereby the earphones <NUM>, <NUM> are connected via wire with a wireless connection to a playback device) or a wired configuration (whereby at least one of the earphones <NUM>, <NUM> has a wired connection to a playback device). Each earphone <NUM>, <NUM> is shown including a body <NUM>, which can include a casing formed of one or more plastics or composite materials. The body <NUM> can include a nozzle <NUM> for insertion into a user's ear canal entrance and a support member <NUM> for retaining the nozzle <NUM> in a resting position within the user's ear. Each earphone <NUM>, <NUM> includes a de-noise system <NUM> for implementing some or all of the various de-noise functions described herein. Other wearable device forms could likewise be implemented, including around-the-ear headphones, over-the-ear headphones, audio eyeglasses, open-ear audio devices etc..

As described herein, various implementations include systems configured to distribute de-noise processing between wearable audio devices and connected accessory devices. Distributed processing allows computational resources to be located in an optimal manner to effectively manage power consumption, cost, form factors, etc..

It is understood that one or more of the functions of the described distributed machine learning-based systems may be implemented as hardware and/or software, and the various components may include communications pathways that connect components by any conventional means (e.g., hard-wired and/or wireless connection). For example, one or more non-volatile devices (e.g., centralized or distributed devices such as flash memory device(s)) can store and/or execute programs, algorithms and/or parameters for one or more described devices. Additionally, the functionality described herein, or portions thereof, and its various modifications (hereinafter "the functions") can 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 apparatus, e.g., a programmable processor, a computer, multiple computers, and/or programmable logic components.

Actions associated with implementing all or part of the functions can be performed by one or more programmable processors executing one or more computer programs to perform the functions. All or part of the functions can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit). Generally, a processor may receive instructions and data from a read-only memory or a random access memory or both. Components of a computer include a processor for executing instructions and one or more memory devices for storing instructions and data.

It is noted that while the implementations described herein utilize microphone systems to collect input signals, it is understood that any type of sensor can be utilized separately or in addition to a microphone system to collect input signals, e.g., accelerometers, thermometers, optical sensors, cameras, etc..

Additionally, actions associated with implementing all or part of the functions described herein can be performed by one or more networked computing devices. Networked computing devices can be connected over a network, e.g., one or more wired and/or wireless networks such as a local area network (LAN), wide area network (WAN), personal area network (PAN), Internet-connected devices and/or networks and/or a cloud-based computing (e.g., cloud-based servers).

Claim 1:
A system (<NUM>;<NUM>) for processing audio signals, comprising:
a wearable audio device (<NUM>;<NUM>;<NUM>) comprising a transducer (<NUM>;<NUM>) and a communication system (<NUM>;<NUM>); and
an accessory device (<NUM>;<NUM>) configured to wirelessly communicate with the wearable audio device, the accessory device comprising a processor (<NUM>;<NUM>) configured to process a source audio signal according to a method that comprises:
source separating the source audio signal; and
providing a source separated audio signal (<NUM>;<NUM>) to the wearable audio device for transduction,
wherein source separating the source audio signal comprises:
converting the source audio signal into a first sequence of frequency transformed frames, each having a first frame size;
converting the source audio signal into a second sequence of frequency transformed frames, each having a second frame size greater than the first frame size;
applying a machine learning model (<NUM>;<NUM>;<NUM>) to the second sequence of frequency transformed frames to create a spectral mask;
applying the spectral mask to the first sequence of frequency transformed frames to generate a de-noised sequence of frequency transformed frames of the first frame size; and
converting the de-noised sequence of frequency transformed frames to a de-noised time domain signal,
wherein providing the de-noised audio signal to the wearable audio device comprises wirelessly transmitting the de-noised time domain signal to the wearable audio device.