Abstract: The present invention relates to the field of textiles. More specifically, it pertains to a process for producing and using fibrillated biodegradable microfibers. The process involves sourcing and preliminary processing plant materials encompassing cellulose-rich fibers followed by mechanical compression method or a solar powered electric presser to expel surplus moisture. Then, an aqueous immersion technique is employed for thermal processing to obtain thermal processed plant materials. The thermal processed plant materials then undergo a rinsing step and a mechanical disintegration step to reduce the thermal processed plant materials into micro-sized entities while maintaining the integrity of the fibers. Furthermore, matrix forming substances, stabilizer, sealant and preservative to said micro-sized entities subsequently undergoing a blending process to obtain a first mixture.

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: 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: 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: According to some embodiments, an ear-worn device, e.g., a hearing aid, is provided that operates to monitor the health of a wearer of the ear-worn device based on an audio signal detected by the ear-worn device. In some embodiments, a method of monitoring the health of the wearer of the ear-worn device includes: detecting the audio signal with a microphone of the ear-worn device; processing the detected audio signal using a processor of the ear-worn device; and outputting, from an output signal generator of the ear-worn device, a generated output audio signal. In some embodiments, processing the detected audio signal includes: extracting data indicative of a health event associated with the wearer by processing the detected audio signal with a machine learning model; generating the output audio signal based on the detected audio signal; and outputting the data indicative of the health event.

Abstract: The disclosure generally relates to a method, system and apparatus for processing audio through a neural network contained in a hearing device. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

Abstract: Ear-worn devices such as hearing aids are described herein. The ear-worn devices include a machine learning model for enhancing received audio signals to be played out to the wearer. The machine learning model is re-trainable based on the received audio signals. The ear-worn device is configured to selectively store received audio signals to be used in re-training the machine learning model.

Abstract: Disclosed herein is wide dynamic range compression (WDRC) circuitry that may be configured to determine a WDRC gain based on a level of an input audio signal and apply the WDRC gain to an enhanced audio signal generated using neural network circuitry. In some embodiments, WDRC circuitry may include speech level calculation circuitry configured to determine a first level, where the first level is calculated based, at least in part, on speech in the input audio signal; level selection circuitry configured to select a level that is the first level if the first level is greater than a threshold level, and a second level, different from the first level, if the first level is not greater than the threshold level; and WDRC gain circuitry configured to determine a WDRC gain based on the level selected by the level selection circuitry.

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: 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.

Abstract: A system may be configured to diagnostically test ear-worn devices when the ear-worn devices are in a charging case by generating test sound signals from receivers of the ear-worn devices, receiving the test sound signals at microphones of the ear-worn devices, determining levels of audio signals originating from the microphones of the ear-worn devices based on receiving the test sound signals, determining, based on the levels of the audio signals originating from the microphones of the ear-worn devices, that there is a fault, and based on determining that there is the fault, generating a notification.

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: 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: The disclosure generally relates to a method, system and apparatus for processing audio through a neural network contained in a hearing device. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

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.

Abstract: A hearing aid includes neural network circuitry configured to implement a neural network trained to separate a speech subsignal and a noise subsignal from an input audio signal, and digital processing circuitry. The digital processing circuitry includes a speech wide dynamic range compression (WDRC) pipeline and a noise WDRC pipeline. The speech WDRC pipeline is configured to apply a set of speech fitting curves to the speech subsignal based at least in part on the level of the speech subsignal. The noise WDRC pipeline is configured to apply a set of noise fitting curves to the noise subsignal based at least in part on the level of the noise subsignal. The set of speech fitting curves is different from the set of noise fitting curves.

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.