Abstract: In some embodiments, an ear-worn device may include adaptive mixing control circuitry configured to control mixing circuitry to mix a speech component of an input audio signal with a noise component of the input audio signal based on a deviation of a short-term level of the noise component of the input audio signal from a long-term level of the noise component of the input audio signal. In some embodiments, an ear-worn device may include beamforming circuitry configured to generate a front audio signal and a back audio signal, and direction-of-arrival circuitry configured to bias an output audio signal based on a difference between the front audio signal and the back audio signal.

Abstract: A hearing aid includes a non-programmable user input device that is not programmable by a user nor by an audiologist, control circuitry configured to receive an activation signal from the non-programmable user input device, one or more microphones, neural network circuitry configured to denoise audio signals received by the one or more microphones, and communication circuitry. The hearing aid is configured to detect, using the control circuitry, user activation of the non-programmable user input device on the hearing aid; control, using the control circuitry and based on detecting the activation of the non-programmable user input device on the hearing aid, switching between enabling and disabling the neural network circuitry; and transmit, using the communication circuitry, an indication of the enabling or disabling of the neural network circuitry to a processing device in operative communication with the hearing aid.

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 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: Aspects of the technology described herein relate to techniques for calculating, during imaging, a quality of a sequence of images collected during the imaging. Calculating the quality of the sequence of images may include calculating a probability that a medical professional would use a given image for clinical evaluation and a confidence that an automated analysis segmentation performed on the given image is correct. Techniques described herein also include receiving a trigger to perform an automatic measurement on a sequence of images, calculating a quality of the sequence of images, determining whether the quality of the sequence of images exceeds a threshold quality, and performing the automatic measurement on the sequence of images based on determining that the quality of the sequence of images exceeds the threshold quality.

Abstract: According to some embodiments, an ear-worn device, e.g., a hearing aid, is provided that operates to enhance audio signals detected by the ear-worn device. In some embodiments, the ear-worn device includes a microphone, a processing circuit coupled to the microphone, and an output signal generator coupled to the processing circuit. In some embodiments, a method for enhancing audio signals includes: detecting an audio signal with the microphone; as the audio signal is being detected, dividing the audio signal into a plurality of segments; enhancing the detected audio signal with the processing circuit of the hearing aid; and outputting the enhanced audio signal with the output signal generator. In some embodiments, enhancing the detected audio signal includes processing one or more of the segments of the detected audio signal with a neural network engine (NNE) of the processing circuit to obtain an output for enhancing the detected 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 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 includes a non-programmable user input device that is not programmable by a user nor by an audiologist, control circuitry configured to receive an activation signal from the non-programmable user input device, one or more microphones, neural network circuitry configured to denoise audio signals received by the one or more microphones, and communication circuitry. The hearing aid is configured to detect, using the control circuitry, user activation of the non-programmable user input device on the hearing aid; control, using the control circuitry and based on detecting the activation of the non-programmable user input device on the hearing aid, switching between enabling and disabling the neural network circuitry; and transmit, using the communication circuitry, an indication of the enabling or disabling of the neural network circuitry to a processing device in operative communication with the hearing aid.

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 is provided that operates to isolate and individually treat the received speech of a target speaker or multiple target speakers from an audio input signal detected in a multi-speaker environment. The ear-worn device uses a machine learning model that receives a voice signature of each of one or more target speakers as input signals, to identify and isolate the component of the audio input signal attributable to the target speaker(s). Once isolated, the target speaker's speech may be enhanced, de-emphasized, or otherwise processed in a manner desired by the wearer of the ear-worn device. The wearer may use an external electronic device, e.g., a phone, to select one or more target speakers in a conversation and/or configure various settings associated with processing the speech on the ear-worn device.

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 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: The systems and methods, in one embodiment, include a convolutional neural network (CNN) model trained by a modified version of the CycleGAN process. The CNN filters ultrasound images generated by a handheld ultrasound device to generate images that are perceptually similar to images generated by a cart-based ultrasound device in terms of quality. The resulting images look sharper and less noisy compared to the original inputs. The invention is a tool within the actions menu of a mobile app. When the tool is open, users can turn filtering on and off. The users will turn filtering on to reduce noise so they can reach the right scanning spot faster. After which because of body habitus, still there might be some noise that this tool can clean up. The user can then decide to have the filtering on or off during the diagnostic process.

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: 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 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