Abstract: An effective howling prevention algorithm may provide improved sound quality of active noise cancellation (ANC) in audio amplification systems, such as in hearing assistance devices. A hearing device includes an inward-facing microphone and receiver, with a controller configured to monitor coherence between microphone and feedback signals to detect potential howling. When howling is detected based on an indicator of instability exceeding a threshold, the controller rapidly converges the gain to zero to prevent feedback howling. After a predetermined delay period without detected howling, the controller slowly converges the gain back to unity to restore active noise cancellation. This approach provides advantages over directly equating coherence to gain by more effectively preventing howling while maintaining optimal noise cancellation performance when stable.

Abstract: A method of saving audio data on an ear-worn device is provided. The method comprises connecting an ear-worn device to an external device; receiving a request for audio data to be sent from the external device to the ear-worn device; modifying a first audio data file on the external device to create a plurality of audio tokens, wherein the data size of the plurality of tokens is smaller than the first audio data file; sending the audio tokens from the external device to the ear-worn device; and saving the audio tokens in a non-volatile memory element of the ear-worn device. Modifying the audio data on the external device can include utilizing a neural audio codec. This method can enable efficient compression and storage of audio data suitable for playback on ear-worn devices with limited memory capacity.

Abstract: Disclosed herein, among other things, are systems and methods for sound pressure level measurement for hearing devices. Various aspects include a method for sound pressure level measurement using self-mixing interferometry. The method includes directing a coherent light signal towards an eardrum of a user using an optical generation device, directing an audio signal towards the eardrum of the user using an audio generation device, and sensing a reflected light signal from the eardrum of the user using an optical sensor device. Displacement of the eardrum caused by the audio signal is computed using the reflected light signal, and real ear sound pressure level (SPL) at the eardrum is computed based on the displacement of the eardrum.

Abstract: A hearing device comprises at least one microphone, a receiver or speaker, and a controller comprising one or more processors configured to implement a sparsity compression scheme. The controller, when implementing the sparsity compression scheme, is configured to apply a fast and high degree of compression to an input signal received by the microphone to provide a compressed signal, filter the input signal by which intense portions of the input signal above a threshold are assigned a high gain value and less intense portions of the input signal below the threshold are assigned a low gain value, the high and low gain values defining sparsity gains, and apply the sparsity gains to the compressed signal to produce an output signal communicated to the receiver or speaker.

Abstract: Embodiments are directed to measuring, on a periodic basis by a hearing device, one or more transfer functions, each of the one or more transfer functions comprising a receiver-to-microphone acoustic transfer function of the hearing device. A plurality of health indicators of the hearing device are calculated over time using a measured characteristic of each of the measured transfer functions and a nominal characteristic of each of the measured transfer functions. Using the plurality of health indicators, degradation or failure of any of the receivers, any of the microphones, or a component in an acoustic path between one or more receivers and one or more microphones is detected. One or both of recommended actions and warnings are generated in response to detecting degradation or failure of any of the receivers, any of the microphones, or the component.

Abstract: An audio input signal is digitized via circuitry of an ear-wearable device. An adaptive feedback canceller has an adaptive filter producing an output that is inserted into the digitized audio input signal to cancel feedback. A motion detector provides a motion signal indicative of motion of the ear-wearable device. A processor is operable to determine a change in a feedback path based on the motion signal. The processor causes the adaptive filter to have faster adaption in response to the change in the feedback path is above a first threshold. The processor also causes the adaptive filter to have slower adaption in response to the change in the feedback path being below a second threshold.

Abstract: A hearing assistance device has an input processing path that receives an audio input signal from a microphone and an output processing path that provides an audio output signal to a receiver. The hearing assistance device further includes a speech enhancement module includes a first neural network trained to enhance speech in the audio input signal and a feedback cancellation module coupled to the speech enhancement module. The feedback cancellation module includes a second neural network trained to represent an acoustic path between the receiver and the microphone. The feedback cancellation module provides a feedback cancellation output that is subtracted from the audio output signal.

Abstract: An audio input signal is digitized via circuitry of an ear-wearable device. An adaptive feedback canceller has an adaptive filter producing an output that is inserted into the digitized audio input signal to cancel feedback. A motion detector provides a motion signal indicative of motion of the car-wearable device. A processor is operable to determine a change in a feedback path based on the motion signal. The processor causes the adaptive filter to have faster adaption in response to the change in the feedback path is above a first threshold. The processor also causes the adaptive filter to have slower adaption in response to the change in the feedback path being below a second threshold.

Abstract: Disclosed herein, among other things, are systems and methods for active noise cancellation (ANC) for hearing device applications. A method includes estimating a first sound pressure on an ear drum of a wearer of the hearing device caused by a hearing processing signal, estimating a second sound pressure on the ear drum of the wearer of the hearing device caused by a leakage path of the hearing device, and computing a ratio of the first sound pressure and the second sound pressure to predict a comb-filtering effect for the hearing device. The method also includes computing an ANC controller using the computed ratio in one or more frequency ranges, and canceling leaked sound into an ear canal of the wearer for the hearing device in the one or more frequency ranges using the ANC controller.

Abstract: Disclosed herein, among other things, are systems and methods for artifact detection and tuning of a feedback canceller. A method includes detecting an artifact of a feedback canceller during operation of a hearing device. Artifact data associated with the detected artifact is stored in a memory of the hearing device, the artifact data including information related to the detected artifact and information related to the hearing device during occurrence of the detected artifact. Artifact statistics are computed based on the stored artifact data, and the artifact data and the artifact statistics are transmitted to an external device. Comparison data is received from the external device, including results of a comparison of artifact data or artifact statistics of the hearing device to artifact data or artifact statistics of other devices. Parameters of the hearing device are automatically adjusted based on the comparison data to improve performance of the feedback canceller.

Abstract: A hearing device includes a microphone that produces an audio input signal and a loudspeaker that outputs an amplified audio signal into an ear canal. A signal processing path is coupled to the microphone and the loudspeaker. The signal processing path includes a deep neural network configured to predict an instantaneous gain margin of the hearing device based on a set of inputs. The set of inputs includes a first parameter of the audio input signal and a second parameter of the amplified audio signal.

Abstract: Disclosed herein, among other things, are systems and methods for active noise cancellation (ANC) for hearing device applications. A method includes estimating a first sound pressure on an ear drum of a wearer of the hearing device caused by a hearing processing signal, estimating a second sound pressure on the ear drum of the wearer of the hearing device caused by a leakage path of the hearing device, and computing a ratio of the first sound pressure and the second sound pressure to predict a comb-filtering effect for the hearing device. The method also includes computing an ANC controller using the computed ratio in one or more frequency ranges, and canceling leaked sound into an ear canal of the wearer for the hearing device in the one or more frequency ranges using the ANC controller.

Abstract: A hearing device includes a deep/recurrent neural network trained to jointly perform sound enhancement and feedback cancellation. During training a neural network is connected between a simulated input and a simulated output of the hearing device. The neural network is operable to change a response affecting the simulated output. The neural network is trained by applying the simulated input to the deep neural network while applying the feedback path response between the simulated input and the simulated output. The deep-neural network is trained to reduce an error between the simulated output and the reference audio signal and used for sound enhancement in the device.

Abstract: A hearing device includes a microphone that produces an audio input signal and a loudspeaker that outputs an amplified audio signal into an ear canal. A signal processing path is coupled to the microphone and the loudspeaker. The signal processing path includes a deep neural network configured to predict an instantaneous gain margin of the hearing device based on a set of inputs. The set of inputs includes a first parameter of the audio input signal, a second parameter of the amplified audio signal, and a gain of the signal processing path. A feedback reduction module of the device receives the predicted instantaneous gain margin and adjusts feedback reduction parameters to reduce an onset of feedback in the hearing device.

Abstract: A hearing device includes a receiver, a microphone, and a proximity sensor operable to detect a proximate object. One or more processors are coupled to the receiver, the microphone, and the proximity sensor. The processors are operable to execute a feedback canceller that cancels feedback transmitted through a feedback path between the receiver and the microphone. The processors are further operable to detect, via the proximity sensor, a relative movement between the object and the hearing device, and in response thereto, adjust a parameter affecting the feedback canceller of the hearing device to compensate for a change caused by the relative movement that affects the feedback canceller, such as a change in the feedback path.

Abstract: Disclosed herein, among other things, are systems and methods for artifact detection and tuning of a feedback canceller. A method includes detecting an artifact of a feedback canceller during operation of a hearing device. Artifact data associated with the detected artifact is stored in a memory of the hearing device, the artifact data including information related to the detected artifact and information related to the hearing device during occurrence of the detected artifact. Artifact statistics are computed based on the stored artifact data, and the artifact data and the artifact statistics are transmitted to an external device. Comparison data is received from the external device, including results of a comparison of artifact data or artifact statistics of the hearing device to artifact data or artifact statistics of other devices. Parameters of the hearing device are automatically adjusted based on the comparison data to improve performance of the feedback canceller.

Abstract: A self-check is initiated via an audio processor circuit of a hearing device while located in a user's ear. In response to the self-check, the device measures a first transfer function of a first feedback path between a receiver of the hearing device and at least one outward facing microphone of the hearing device, and measures a second transfer function of a second feedback path between the receiver and an inward facing microphone of the hearing device, An anomaly is determined in at least one of the first transfer function and the second transfer function, and an otoscopic condition of the ear is detected based on the anomaly.

Abstract: A feedback cancellation function is operable to cancel feedback through a receiver of the hearing device. An audible indicator to a user of the hearing device is emitted via the receiver. The audible indicator has a primary purpose unrelated to characterization of the feedback cancellation function. A response of the audible indicator is detected at a microphone of the hearing device while the hearing device is being worn by the user. Subsequent feedback path characterization data of the hearing device is determined based on the response. The feedback cancellation function uses the subsequent feedback path characterization data.

Abstract: A self-check is initiated via an audio processor circuit of the hearing device. In response to the self-check, a transfer function of a feedback path is measured between a receiver of the hearing device to at least one microphone of the hearing device. An anomaly is determined in the transfer function via comparison with example feedback path characterization data. An abnormality associated with the hearing device is predicted based on the anomaly. An indication of the abnormality is presented via a user interface of the hearing device.

Abstract: An individual cutoff frequency for an individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using an individual secondary path measurement, a reduced dimensionality estimate, and a least squares estimate. A sound pressure level at an eardrum of the user is predicted using the first and second receiver-to-eardrum responses.