Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: An adaptive feedback canceller of an ear-wearable device has an adaptive foreground filter that inserts a feedback cancellation signal into a digitized input signal to produce an error signal. An instability detector of the device is configured to extract wo or more features from the error signal. The instability detector has a machine learning module that determines instability in the error signal based on the two or more features. The instability module changes the adaptive foreground filter in response to determining the instability. The change causes the adaptive foreground filter to have a faster adaptation to perturbations in the error signal compared to a previously used step size.

Abstract: In one example, a method includes determining, by one or more processors of a hearing aid programmer, values for one or more properties of a first hearing aid; obtaining, by the one or more processors, based on feedback cancelation configurations of a plurality of other hearing aids having the same values for the one or more properties as the first hearing aid, a predicted initial feedback cancelation configuration for the first hearing aid; and programming, by the one or more processors, the first hearing aid based on the predicted initial feedback cancelation configuration.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: The present subject matter can improve robustness of performance of acoustic feedback cancellation in the presence of strong acoustic disturbances. In various embodiments, an optimization criterion determined to enhance robustness of an adaptive feedback canceller in an audio device against disturbances in an incoming audio signal can be applied such that the adaptive feedback canceller remains in a converged state in response to presence of the disturbances.

Abstract: A hearing assistance system includes a pair of left and right hearing assistance devices to be worn by a wearer and uses both of the left and right hearing assistance devices to detect the voice of the wearer. The left and right hearing assistance devices each include first and second microphones at different locations. Various embodiments detect the voice of the wearer using signals produced by the first and second microphones of the left hearing assistance device and the first and second microphones of the right hearing assistance device. Various embodiments use outcome of detection of the voice of the wearer performed by the left hearing assistance device and the outcome of detection of the voice of the wearer performed the right hearing assistance device to determine whether to declare a detection of the voice of the wearer.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A local device of an auditory device system is configured to perform a process that switches between operating modes. As part of the process, the local device may determine a wireless quality parameter indicative of a current environment for wireless communication with the contra device. Additionally, based on the one or more local input audio signals and the wireless quality parameter, the local device may determine whether to change a local active operating mode of the local device from a bilateral mode to a binaural mode or from the binaural mode to the bilateral mode. The local device may generate a local output audio signal based on the one or more local input audio signals in accordance with the local active operating mode and may produce sound based on the local output audio signal.

Abstract: A hearing assistance system includes a pair of left and right hearing assistance devices to be worn by a wearer and uses both of the left and right hearing assistance devices to detect the voice of the wearer. The left and right hearing assistance devices each include first and second microphones at different locations. Various embodiments detect the voice of the wearer using signals produced by the first and second microphones of the left hearing assistance device and the first and second microphones of the right hearing assistance device. Various embodiments use outcome of detection of the voice of the wearer performed by the left hearing assistance device and the outcome of detection of the voice of the wearer performed the right hearing assistance device to determine whether to declare a detection of the voice of the wearer.

Abstract: An example of an apparatus configured to be worn by a person who has an ear and an ear canal includes a first microphone adapted to be worn about the ear of the person, and a second microphone adapted to be worn at a different location than the first microphone. The apparatus includes a sound processor adapted to process signals from the first microphone to produce a processed sound signal, a receiver adapted to convert the processed sound signal into an audible signal to the wearer of the hearing assistance device, and a voice detector to detect the voice of the wearer. The voice detector includes an adaptive filter to receive signals from the first microphone and the second microphone.

Abstract: A hearing assistance system including a hearing instrument designated as a master device and at least another hearing instrument designated as a slave device. The master device is communicatively coupled to the slave device via a wireless link. The master device has a master clock and generates master time stamps for specified events timed by the master clock. The master time stamps are sent to the slave device via the wireless link. The slave device has a slave clock and generates slave time stamps for specified events timed by the slave clock. The slave clock is adjusted for synchronization to the master clock using the master time stamps and the slave time stamps.

Abstract: In one example, a method includes determining, by one or more processors of a hearing aid programmer, values for one or more properties of a first hearing aid; obtaining, by the one or more processors, based on feedback cancelation configurations of a plurality of other hearing aids having the same values for the one or more properties as the first hearing aid, a predicted initial feedback cancelation configuration for the first hearing aid; and programming, by the one or more processors, the first hearing aid based on the predicted initial feedback cancelation configuration.

Abstract: A hearing assistance system includes a pair of left and right hearing assistance devices to be worn by a wearer and uses both of the left and right hearing assistance devices to detect the voice of the wearer. The left and right hearing assistance devices each include first and second microphones at different locations. Various embodiments detect the voice of the wearer using signals produced by the first and second microphones of the left hearing assistance device and the first and second microphones of the right hearing assistance device. Various embodiments use outcome of detection of the voice of the wearer performed by the left hearing assistance device and the outcome of detection of the voice of the wearer performed the right hearing assistance device to determine whether to declare a detection of the voice of the wearer.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A local device of an auditory device system is configured to perform a process that switches between operating modes. As part of the process, the local device may determine a wireless quality parameter indicative of a current environment for wireless communication with the contra device. Additionally, based on the one or more local input audio signals and the wireless quality parameter, the local device may determine whether to change a local active operating mode of the local device from a bilateral mode to a binaural mode or from the binaural mode to the bilateral mode. The local device may generate a local output audio signal based on the one or more local input audio signals in accordance with the local active operating mode and may produce sound based on the local output audio signal.

Abstract: In one example, a method includes determining, by one or more processors of a hearing aid programmer, values for one or more properties of a first hearing aid; obtaining, by the one or more processors, based on feedback cancelation configurations of a plurality of other hearing aids having the same values for the one or more properties as the first hearing aid, a predicted initial feedback cancelation configuration for the first hearing aid; and programming, by the one or more processors, the first hearing aid based on the predicted initial feedback cancelation configuration.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing assistance system obtains a first input audio signal that is based on sound received by a first set of microphones. The system also obtains a second input audio signal that is based on sound received by a second, different set of microphones. A first adaptive beamformer generates a first output audio signal based on the first input audio signal, the second input audio signal, and a value of a first parameter. A second adaptive beamformer generates a second output audio signal based on the first input audio signal, the second input audio signal, and a value of a second parameter. The value of the first parameter and the value of the second parameter are determined such that a magnitude squared coherence (MSC) of the first output audio signal and the second output audio signal is less than or equal to the coherence threshold.