Starkey Laboratories, Inc.

University of California

Zhang, Tao

Fitz, Kelly

Klobe, Kirk

Haggerty, David

Abdollahi, Mohammad

Xu, Buye

Kelly R Fitz

Tao Zhang

Brent Edwards

Buye Xu

Carlos Renato Calcada Nakagawa

Mohammad Abdollahi

Andrew W Schmeder

Kirk Klobe

David Wessel

Eric Battenberg

David Haggerty

Donald James Reynolds

Sridhar Kalluri

John Ellison

Deniz Baskent

Martin McKinney

Dean G Meyer

Susie Valentine

JuanJuan Xiang

Ulrike Axen

Juan Juan Xiang

Disclosed herein, among other things, are apparatus and methods for neural network-driven feedback cancellation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device to mitigate entrainment, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained neural network to control acoustic feedback cancellation of the input signal.

Neural network-driven feedback cancellation

Disclosed herein, among other things, are apparatus and methods for neural network-driven frequency translation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to train a processor to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained processor to control frequency translation of the input signal.

Neural network-driven frequency translation

An Internet-connected system and method for adapting a hearing configuration in a smart environment includes a hearing system including a hearing device. The hearing system connects to the Internet and transmits or receives an identification parameter corresponding to the hearing system. The hearing system receives a hearing program parameter over the Internet for configuring the hearing device when the hearing system is within a smart environment defined by a smart space system. The hearing program parameter computed is based on an environmental parameter measured within the smart environment by a sensor system of the smart space system. The hearing program parameter is sent to the hearing system over the Internet in response to a discovery system of the smart space system detecting the presence of the hearing system in the smart environment in response to receiving the identification parameter. The hearing device is programmed based on the hearing program parameter.

Listening experiences for smart environments using hearing devices

Disclosed herein, among other things, are systems and methods for a user adjustment interface using remote computing resources. Specifically, a system can include a mobile device in communication with a hearing assistance device or a remote server. The mobile device can interpret an acoustic environment and send information about the environment to a remote server. The remote server can determine and send information to the mobile device for use in a user interface. The mobile device can receive a user selection of hearing assistance parameter information to be sent to the hearing assistance device.

User adjustment interface using remote computing resource

A hearing aid includes a sound classification module to classify environmental sound sensed by a microphone. The sound classification module executes an advanced sound classification algorithm. The hearing aid then processes the sound according to the classification.

Sound classification system for hearing aids

Disclosed herein, among other things, are apparatus and methods for a binaurally coordinated frequency translation for hearing assistance devices. In various method embodiments, an audio input signal is received at a first hearing assistance device for a wearer. The audio input signal is analyzed and a first set of target parameters is calculated. A third set of target parameters is derived from the first set and a second set of calculated target parameters received from a second hearing assistance device using a programmable criteria, and frequency lowered auditory cues are generated using the third set of target parameters. The derived third set of target parameters is used in both the first hearing assistance device and the second hearing assistance device for binaurally coordinated frequency lowering.

Binaurally coordinated frequency translation in hearing assistance devices

Disclosed herein, among other things, are apparatus and methods for tonality-driven feedback canceler adaptation for hearing devices. Various embodiments include a method of signal processing an input signal in a hearing device to mitigate entrainment, the hearing device including a receiver and a microphone. The method includes detecting strength of tonality of the input signal by estimating a second derivative of subband phase of the input signal, and adjusting parameters of an adaptive feedback canceler of the hearing device based on the detected tonality.

Tonality-driven feedback canceler adaptation

This application relates to a systems and methods for enhanced dynamics processing of streaming audio by source separation and remixing for hearing assistance devices, according to one example. In one embodiment, an external streaming audio device processes sources isolated from an audio signal using source separation, and mixes the resulting signals back into the unprocessed audio signal to enhance individual sources while minimizing audible artifacts. Variations of the present system use source separation in a side chain to guide processing of a composite audio signal.

Enhanced dynamics processing of streaming audio by source separation and remixing

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Eden Prairie, MN, United States of America

Kelly R Fitz