Abstract: An ear-worn device may include two or more microphones configured to generate time-domain audio signals, each of the two or more microphones configured to generate one of the time-domain audio signals; processing circuitry comprising analog processing circuitry, digital processing circuitry, beamforming circuitry, and short-time Fourier transformation (STFT) circuitry, the processing circuitry configured to generate, from the time-domain audio signals, one or more frequency-domain non-beamformed audio signals and one or more frequency-domain beamformed signals; and enhancement circuitry comprising neural network circuitry configured to receive multiple frequency-domain input audio signals originating from the one or more frequency-domain non-beamformed audio signals and the one or more frequency-domain beamformed signals, and implement a single neural network trained to generate, based on the multiple frequency-domain input audio signals, a noise-reduced and spatially-focused output audio signal or an output

Abstract: A neural network chip may include a plurality of tiles including a first tile and a second tile. The first tile may be configured to generate first data at least in part by performing first multiply-accumulate operations. The second tile may be configured to generate second data at least in part by performing second multiply-accumulate operations. The second tile may be configured to transmit a control signal to the first tile when the second data has been generated. The first tile may be configured to transmit the first data to the second tile when the control signal has been received and the first data has been generated. The second tile may be configured to combine the second data generated by the second tile with the first data received from the first tile to produce combined first data and second data.

Abstract: A hearing aid may include a neural network chip having tiles arranged in an array, each tile including memory, 16-128 multiplier-accumulator circuits (MACs), and routing circuitry. The memory of each tile may be configured to store a portion of elements of a matrix A comprising weights of a recurrent neural network. Each tile may be configured to receive and store elements of an activation vector X, and all tiles in a column of the array may be configured to receive the same elements of X. The plurality of tiles may be configured to perform a matrix-vector multiplication A*X by performing multiply-and-accumulate sub-operations in parallel among the plurality of tiles. The routing circuitry from the tiles in each respective row of tiles may be configured to combine results of the multiply-and-accumulate sub-operations.

Abstract: An ear-worn device may include two or more microphones configured to generate time-domain audio signals, each of the two or more microphones configured to generate one of the time-domain audio signals; processing circuitry comprising analog processing circuitry, digital processing circuitry, beamforming circuitry, and short-time Fourier transformation (STFT) circuitry, the processing circuitry configured to generate, from the time-domain audio signals, one or more frequency-domain non-beamformed audio signals and one or more frequency-domain beamformed signals; and enhancement circuitry comprising neural network circuitry configured to receive multiple frequency-domain input audio signals originating from the one or more frequency-domain non-beamformed audio signals and the one or more frequency-domain beamformed signals, and implement a single neural network trained to generate, based on the multiple frequency-domain input audio signals, a noise-reduced and spatially-focused output audio signal or an output

Abstract: A hearing aid may include a neural network chip having tiles arranged in an array, each tile including memory, 16-128 multiplier-accumulator circuits (MACs), and routing circuitry. The memory of each tile may be configured to store a portion of elements of a matrix A comprising weights of a recurrent neural network. Each tile may be configured to receive and store elements of an activation vector X, and all tiles in a column of the array may be configured to receive the same elements of X. The plurality of tiles may be configured to perform a matrix-vector multiplication A*X by performing multiply-and-accumulate sub-operations in parallel among the plurality of tiles. The routing circuitry from the tiles in each respective row of tiles may be configured to combine results of the multiply-and-accumulate sub-operations.

Abstract: An apparatus (e.g., an ear-worn device such as a hearing aid) includes neural network circuitry and control circuitry. The neural network circuitry is configured to implement a neural network system comprising at least a first neural network and a second neural network operating in parallel. The control circuitry is configured to control the neural network system to receive a first input signal, process the first input signal using the first neural network to produce a first output and using the second neural network to produce a second output, combine the first output and the second output, reset one or more states of the first neural network, and reset one or more states of the second neural network at a different time than when the one or more states of the first neural network are reset.

Abstract: A hearing aid may include a neural network chip having tiles arranged in an array, each tile including memory, 16-128 multiplier-accumulator circuits (MACs), and routing circuitry. The memory of each tile may be configured to store a portion of elements of a matrix A comprising weights of a recurrent neural network. Each tile may be configured to receive and store elements of an activation vector X, and all tiles in a column of the array may be configured to receive the same elements of X. The plurality of tiles may be configured to perform a matrix-vector multiplication A*X by performing multiply-and-accumulate sub-operations in parallel among the plurality of tiles. The routing circuitry from the tiles in each respective row of tiles may be configured to combine results of the multiply-and-accumulate sub-operations.

Abstract: A neural network chip may include a weight memory configured to store multiple words, each word of the multiple words including a weight field configured to store multiple compressed neural network weights including all bits of M compressed neural network weights and some but not all bits of an (M+1)th compressed neural network weight, and an identifier field configured to store an identifier. A router may be configured to route a word of the multiple words from the weight memory to one of multiple queues based on the identifier in the identifier field of the word. A decompression engine may be configured to pop a compressed neural network weight at a head of a respective queue from the respective queue when compressed neural network weights at heads of all of the multiple queues are valid, and decompress the compressed neural network weight.

Abstract: A system may include a first ear-worn device worn by a first wearer and a second ear-worn device worn by a second wearer, in which the first ear-worn device is configured to generate an audio signal based on sound from its environment, generate a speech portion of the audio signal using a neural network-based noise-reduction pipeline in the first ear-worn device, and transmit the speech portion of the audio signal to the second ear-worn device. The first ear-worn device may be configured to determine whether to transmit the speech portion of the audio signal to the second ear-worn device based on an own-voice heuristic. The second ear-worn device may be configured to generate an output audio signal by applying a first weight to the speech portion of the first ear-worn device's audio signal and a second weight to the speech portion of its own audio signal.

Abstract: An ear-worn device may include two or more microphones configured to generate time-domain audio signals, each of the two or more microphones configured to generate one of the time-domain audio signals; processing circuitry comprising analog processing circuitry, digital processing circuitry, beamforming circuitry, and short-time Fourier transformation (STFT) circuitry, the processing circuitry configured to generate, from the time-domain audio signals, one or more frequency-domain non-beamformed audio signals and one or more frequency-domain beamformed signals; and enhancement circuitry comprising neural network circuitry configured to receive multiple frequency-domain input audio signals originating from the one or more frequency-domain non-beamformed audio signals and the one or more frequency-domain beamformed signals, and implement a single neural network trained to generate, based on the multiple frequency-domain input audio signals, a noise-reduced and spatially-focused output audio signal or an output

Abstract: An apparatus (e.g., an ear-worn device such as a hearing aid) includes neural network circuitry and control circuitry. The neural network circuitry is configured to implement a neural network system comprising at least a first neural network and a second neural network operating in parallel. The control circuitry is configured to control the neural network system to receive a first input signal, process the first input signal using the first neural network to produce a first output and using the second neural network to produce a second output, combine the first output and the second output, reset one or more states of the first neural network, and reset one or more states of the second neural network at a different time than when the one or more states of the first neural network are reset.

Abstract: An apparatus (e.g., an ear-worn device such as a hearing aid) may include neural network circuitry and control circuitry. The neural network circuitry may be configured to implement a neural network system comprising at least a first neural network and a second neural network operating in parallel. The control circuitry may be configured to control the neural network system to receive a first input signal, process the first input signal using the first neural network to produce a first output and using the second neural network to produce a second output, combine the first output and the second output, reset one or more states of the first neural network, and reset one or more states of the second neural network at a different time than when the one or more states of the first neural network are reset.

Abstract: An ear-worn device may include two or more microphones configured to generate time-domain audio signals, each of the two or more microphones configured to generate one of the time-domain audio signals; processing circuitry comprising analog processing circuitry, digital processing circuitry, beamforming circuitry, and short-time Fourier transformation (STFT) circuitry, the processing circuitry configured to generate, from the time-domain audio signals, one or more frequency-domain non-beamformed audio signals and one or more frequency-domain beamformed signals; and enhancement circuitry comprising neural network circuitry configured to receive multiple frequency-domain input audio signals originating from the one or more frequency-domain non-beamformed audio signals and the one or more frequency-domain beamformed signals, and implement a single neural network trained to generate, based on the multiple frequency-domain input audio signals, a noise-reduced and spatially-focused output audio signal or an output

Abstract: A hearing aid may include a neural network chip having tiles arranged in an array, each tile including memory, 16-128 multiplier-accumulator circuits (MACs), and routing circuitry. The memory of each tile may be configured to store a portion of elements of a matrix A comprising weights of a recurrent neural network. Each tile may be configured to receive and store elements of an activation vector X, and all tiles in a column of the array may be configured to receive the same elements of X. The plurality of tiles may be configured to perform a matrix-vector multiplication A*X by performing multiply-and-accumulate sub-operations in parallel among the plurality of tiles. The routing circuitry from the tiles in each respective row of tiles may be configured to combine results of the multiply-and-accumulate sub-operations.

Abstract: A hearing aid may include a neural network chip having tiles arranged in an array, each tile including memory, 16-128 multiplier-accumulator circuits (MACs), and routing circuitry. The memory of each tile may be configured to store a portion of elements of a matrix A comprising weights of a recurrent neural network. Each tile may be configured to receive and store elements of an activation vector X, and all tiles in a column of the array may be configured to receive the same elements of X. The plurality of tiles may be configured to perform a matrix-vector multiplication A*X by performing multiply-and-accumulate sub-operations in parallel among the plurality of tiles. The routing circuitry from the tiles in each respective row of tiles may be configured to combine results of the multiply-and-accumulate sub-operations.

Abstract: An ear-worn device may include two or more microphones configured to generate time-domain audio signals, each of the two or more microphones configured to generate one of the time-domain audio signals; processing circuitry comprising analog processing circuitry, digital processing circuitry, beamforming circuitry, and short-time Fourier transformation (STFT) circuitry, the processing circuitry configured to generate, from the time-domain audio signals, one or more frequency-domain non-beamformed audio signals and one or more frequency-domain beamformed signals; and enhancement circuitry comprising neural network circuitry configured to receive multiple frequency-domain input audio signals originating from the one or more frequency-domain non-beamformed audio signals and the one or more frequency-domain beamformed signals, and implement a single neural network trained to generate, based on the multiple frequency-domain input audio signals, a noise-reduced and spatially-focused output audio signal or an output

Abstract: A hearing aid may include a neural network chip having tiles arranged in an array, each tile including memory, 16-128 multiplier-accumulator circuits (MACs), and routing circuitry. The memory of each tile may be configured to store a portion of elements of a matrix A comprising weights of a recurrent neural network. Each tile may be configured to receive and store elements of an activation vector X, and all tiles in a column of the array may be configured to receive the same elements of X. The plurality of tiles may be configured to perform a matrix-vector multiplication A*X by performing multiply-and-accumulate sub-operations in parallel among the plurality of tiles. The routing circuitry from the tiles in each respective row of tiles may be configured to combine results of the multiply-and-accumulate sub-operations.

Abstract: An apparatus (e.g., an ear-worn device such as a hearing aid) includes neural network circuitry and control circuitry. The neural network circuitry is configured to implement a neural network system comprising at least a first neural network and a second neural network operating in parallel. The control circuitry is configured to control the neural network system to receive a first input signal, process the first input signal using the first neural network to produce a first output and using the second neural network to produce a second output, combine the first output and the second output, reset one or more states of the first neural network, and reset one or more states of the second neural network at a different time than when the one or more states of the first neural network are reset.

Abstract: The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal.

Abstract: The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal.