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 two 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:
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.
Abstract:
A hearing assistance system includes an adaptive binaural beamformer based on a multichannel Wiener filter (MWF) optimized for noise reduction and speech quality criteria using a priori spatial information. In various embodiments, the optimization problem is formulated as a quadratically constrained quadratic program (QCQP) aiming at striking an appropriate balance between these criteria. In various embodiments, the MWF executes a low-complexity iterative dual decomposition algorithm to solve the QCQP formulation.
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 controller remains in a converged state in response to presence of the disturbances.
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.