Abstract: Described herein is binaural data sharing technology for ear-worn devices to improve audio processing performance. Different embodiments may include sharing of various data types, such as processed microphone signals, beamformed signals, neural network products (e.g., masks), and environmental metrics. For beamforming, devices may combine signals from both ears for improved directional selectivity or process separate beamformed signals independently. Devices may be configured to generate identical masks or average mask magnitude portions while preserving device-specific phase components. Neural networks may be trained to handle mixed-latency data, processing current local data with “stale” data from the other device. Environmental metrics like signal-to-noise ratios may be shared for coordinated responses to acoustic conditions. The technology may also apply to integrated devices like eyeglasses.

Abstract: Described herein is an ear-worn device that may include processing circuitry and control circuitry. The processing circuitry may include neural network circuitry configured to implement one or more first neural network layers or one or more second neural network layers. The control circuitry may be configured to control the processing circuitry to operate using a first configuration or a second configuration. The neural network circuitry may be configured to implement the one or more first neural network layers when the processing circuitry operates using the first configuration, and the first configuration may have a first data processing latency. The neural network circuitry may be configured to implement the one or more second neural network layers when the processing circuitry operates using the second configuration, and the second configuration may have a second data processing latency different from the first data processing latency.

Abstract: Described herein is binaural data sharing technology for ear-worn devices to improve audio processing performance. Different embodiments may include sharing of various data types, such as processed microphone signals, beamformed signals, neural network products (e.g., masks), and environmental metrics. For beamforming, devices may combine signals from both ears for improved directional selectivity or process separate beamformed signals independently. Devices may be configured to generate identical masks or average mask magnitude portions while preserving device-specific phase components. Neural networks may be trained to handle mixed-latency data, processing current local data with “stale” data from the other device. Environmental metrics like signal-to-noise ratios may be shared for coordinated responses to acoustic conditions. The technology may also apply to integrated devices like eyeglasses.

Abstract: An ear-worn device includes two or more microphones and noise reduction circuitry including neural network circuitry. The neural network circuitry is configured to: receive multiple audio signals wherein at least two of the multiple audio signals each originate from a different one of the two or more microphones and/or at least one of the multiple audio signals is a beamformed audio signal originating from the two or more microphones; and implement one or more neural network layers trained to perform background noise modification and spatial focusing based on the multiple audio signals, such that the neural network circuitry generates, based on the multiple audio signals, one or more neural network outputs. The noise reduction circuitry is configured to output, based on the one or more neural network outputs, an output audio signal comprising a background noise-modified and spatially-focused version of a first audio signal of the multiple audio signals.

Abstract: An ear-worn device may include neural network circuitry configured to determine, using a neural network, one or both of a neural network-predicted speech component of an input audio signal and a neural network-predicted noise component of the input audio signal. The ear-worn device may further include mixing circuitry configured to perform mixing resulting in an output signal corresponding to the neural network-predicted speech component of the input audio signal mixed with the neural network-predicted noise component of the input audio signal. The ear-worn device may further include adaptive mixing control circuitry configured to control the mixing performed by the mixing circuitry based, at least in part, on a level of an estimate of a stationary noise component of the input audio signal and/or a level of the neural network-predicted noise component of the input audio signal.

Abstract: An ear-worn device includes two or more microphones and noise reduction circuitry including neural network circuitry. The neural network circuitry is configured to: receive multiple audio signals wherein at least two of the multiple audio signals each originate from a different one of the two or more microphones and/or at least one of the multiple audio signals is a beamformed audio signal originating from the two or more microphones; and implement one or more neural network layers trained to perform background noise modification and spatial focusing based on the multiple audio signals, such that the neural network circuitry generates, based on the multiple audio signals, one or more neural network outputs. The noise reduction circuitry is configured to output, based on the one or more neural network outputs, an output audio signal comprising a background noise-modified and spatially-focused version of a first audio signal of the multiple audio signals.

Abstract: An ear-worn device includes two or more microphones and noise reduction circuitry including neural network circuitry. The neural network circuitry is configured to: receive multiple audio signals wherein at least two of the multiple audio signals each originate from a different one of the two or more microphones and/or at least one of the multiple audio signals is a beamformed audio signal originating from the two or more microphones; and implement one or more neural network layers trained to perform background noise modification and spatial focusing based on the multiple audio signals, such that the neural network circuitry generates, based on the multiple audio signals, one or more neural network outputs. The noise reduction circuitry is configured to output, based on the one or more neural network outputs, an output audio signal comprising a background noise-modified and spatially-focused version of a first audio signal of the multiple audio signals.

Abstract: An ear-worn device includes two or more microphones and noise reduction circuitry including neural network circuitry. The neural network circuitry is configured to: receive multiple audio signals wherein at least two of the multiple audio signals each originate from a different one of the two or more microphones and/or at least one of the multiple audio signals is a beamformed audio signal originating from the two or more microphones; and implement one or more neural network layers trained to perform background noise modification and spatial focusing based on the multiple audio signals, such that the neural network circuitry generates, based on the multiple audio signals, one or more neural network outputs. The noise reduction circuitry is configured to output, based on the one or more neural network outputs, an output audio signal comprising a background noise-modified and spatially-focused version of a first audio signal of the multiple audio signals.

Abstract: An ear-worn device includes two or more microphones and noise reduction circuitry including neural network circuitry. The neural network circuitry is configured to: receive multiple audio signals wherein at least two of the multiple audio signals each originate from a different one of the two or more microphones and/or at least one of the multiple audio signals is a beamformed audio signal originating from the two or more microphones; and implement one or more neural network layers trained to perform background noise modification and spatial focusing based on the multiple audio signals, such that the neural network circuitry generates, based on the multiple audio signals, one or more neural network outputs. The noise reduction circuitry is configured to output, based on the one or more neural network outputs, an output audio signal comprising a background noise-modified and spatially-focused version of a first audio signal of the multiple audio signals.