Hearing device with feedback instability detector that changes an adaptive filter
AssigneeStarkey Laboratories, Inc.
InventorsWenyu Jin, Ivo Merks
Life of this patent
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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.
Claims21
1-40. (canceled)
An ear-wearable device, comprising:
- an input sensor that provides an input signal, the input signal being digitized via circuitry of the ear-wearable device;
- an adaptive feedback canceller comprising an adaptive foreground filter that inserts a feedback cancellation signal into the digitized input signal to produce an error signal;
- and
- an instability detector configured to extract two or more features from the error signal, the instability detector comprising a machine learning module that determines an instability in the error signal based on the two or more features, the instability module changing a step size of the adaptive foreground filter in response to determining the instability, the changed step size causing the adaptive foreground filter to have a faster adaptation to perturbations in the error signal compared to a previously used step size.
A method comprising:
- extracting two or more features from an error signal of a feedback cancellation loop of an ear-wearable device, the two or more features comprising a power-level-dependent feature and a power-level-independent feature;
- inputting the two or more features to a machine learning module to determine an instability in the error signal;
- and
- changing a step size of an adaptive foreground filter used to cancel feedback in the ear-wearable device in response to determining the instability, the changed step size causing the adaptive foreground filter to have a faster adaptation to perturbations in the error signal compared to a previously used step size.
A method comprising:
- extracting two or more features from an error signal of a feedback cancellation loop of an ear-wearable device, the two or more features comprising a power-level-dependent feature and a power-level-independent feature;
- inputting the two or more features to a machine learning module to determine an instability in the error signal;
- and
- changing an optimization algorithm of an adaptive foreground filter used to cancel feedback in the ear-wearable device in response to determining the instability, the changed optimization algorithm having a faster adaptation to perturbations in the error signal compared to a previously used optimization algorithm.
Dependencies are inferred from the claim text.