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: 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 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 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 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: 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: Aspects of the technology described herein relate to configuring an ultrasound system with imaging parameter values. In particular, certain aspects relate to configuring an ultrasound system to produce a plurality of sets of ultrasound images, each respective set of the plurality of sets of ultrasound images being produced with a different respective set of a plurality of sets of imaging parameter values; obtaining, from the ultrasound system, the plurality of sets of ultrasound images; determining a set of ultrasound images from among the plurality of sets of ultrasound images that has a highest quality; and based on determining the set of ultrasound images from among the plurality of sets of ultrasound images that has the highest quality, automatically configuring the ultrasound system to produce ultrasound images using a set of imaging parameter values with which the set of ultrasound images that has the highest quality was produced.

Abstract: Methods and apparatuses for providing indications of missing landmarks in ultrasound images are described. Some embodiments are directed to apparatuses comprising a processing device configured to obtain data representing an ultrasound image, and determine whether the ultrasound image is clinically usable, wherein the determining comprises determining whether the ultrasound image lacks one or more landmarks. Determining whether the ultrasound image is clinically usable may further comprise determining a quality value representative of a quality of the ultrasound image and comparing the quality value to a threshold quality value. In some embodiments, landmarks comprise one or more anatomical features, such as a rib, a pleural line and an A line, a liver, and a kidney.

Abstract: Aspects of the technology described herein relate to configuring an ultrasound system with imaging parameter values. In particular, certain aspects relate to configuring an ultrasound system to produce a plurality of sets of ultrasound images, each respective set of the plurality of sets of ultrasound images being produced with a different respective set of a plurality of sets of imaging parameter values; obtaining, from the ultrasound system, the plurality of sets of ultrasound images; determining a set of ultrasound images from among the plurality of sets of ultrasound images that has a highest quality; and based on determining the set of ultrasound images from among the plurality of sets of ultrasound images that has the highest quality, automatically configuring the ultrasound system to produce ultrasound images using a set of imaging parameter values with which the set of ultrasound images that has the highest quality was produced.