Bose Corporation

Northwestern University

Andrew Todd Sabin

Jahn Dmitri Eichfeld

Marko Stamenovic

Benjamin Davis Parker

Ryan terMeulen

Fernando Mier

Daniel M Gauger, Jr

Daniel Ross Tengelsen

Carl Ralph Jensen

Bryan A Pardo

Ryan C Struzik

Andrew D Dominijanni

John Allen Rule

Wade P Torres

Dale McElhone

Keith Dana Martin

William M Rabinowitz

John Andrew Trotter

Andrew Jackson Stockton, X

Brian J Maguire

Matthew Eliot Neutra

Li-Chia Yang

Aric J Wax

Adam C Furman

Steven Edward Munley

Robert Heinz Haslinger

Keith Robert Santarelli

Ricardo Garcia

Michelle Lee Daniels

Patrick William Buzza

Andrew Jackson Stockton X

Crystal P MacKenzie

Colin D Fletcher

A head-worn acoustic device includes at least one acoustic transducer disposed such that, in a head-worn state, the transducer is in an open-ear configuration in which an ear canal of a user of the head-worn acoustic device is unobstructed. The acoustic device also includes at least one microphone configured to capture audio that is processed and played back through the transducer, and an amplifier circuit configured to process signals representing the audio captured using the microphone and generate driver signals for the transducer. The transducer and the microphone are disposed on the head-worn acoustic device such that, in the head-worn state, a lobe of a radiation pattern of the at least one acoustic transducer is directed towards the ear canal of the user, and the at least one microphone is positioned in an acoustic null in a radiation pattern of the at least one acoustic transducer.

Microphone placement in open ear hearing assistance devices

A system includes an in-ear acoustic device that is configured to sit at least partially within a user's ear canal and a head-worn electronic device that is supported on a user's body outside of the user's ear canal. The in-ear acoustic device includes a first receiver and a first coil. The head-worn electronic device includes a second coil that is configured to communicate with the first coil via near-field magnetic inductance (NFMI) communication. The head-worn electronic device is configured to transmit a first processed audio signal to the in-ear acoustic device via NFMI communication, and the first processed audio signal is used to drive the first receiver.

Near-field magnetic inductance communication with in-ear acoustic devices

Techniques for user authentication via in-ear acoustic measurements are disclosed. An in-ear device emits an acoustic signal and detects an otic response to the acoustic signal. A user signature is generated based at least on the otic response to the acoustic signal. The user signature is compared with a reference signature, to determine whether the in-ear device has changed users.

User authentication via in-ear acoustic measurements

Various aspects include approaches for managing tinnitus in a user. In some particular aspects, a computer-implemented method of managing tinnitus of a user with at least one wearable device includes: actuating, according to a prescribed pattern and using the at least one wearable device: a) an electro-acoustic transducer to provide an audio output to the user, and b) a contact element to apply vibration to a body part of the user, where the prescribed pattern both stimulates the nervous system of the user and delivers a sound to the user, which together mitigate the tinnitus.

Haptic tinnitus therapeutic system

Various implementations include conversation assistance audio devices with settings that are adjustable based upon user feedback. In some cases, a computer-implemented method of personalizing a conversation assistance audio device includes: receiving a command from a user for assistance with audio settings in the conversation assistance audio device; applying a baseline set of audio settings to the conversation assistance audio device in response to receiving the user command for assistance; receiving a user command to change the baseline set of audio settings; and applying a modified set of audio settings to the conversation assistance audio device in response to receiving the user command to change the baseline set of audio settings, wherein the modified set of audio settings are applied based upon usage of the conversation assistance audio device and a population model of usage of similar conversation assistance audio devices.

Conversation assistance audio device personalization

Various implementations include systems for processing audio signals. In particular implementations, a process includes receiving an audio signal, wherein the audio signal includes a speech component of the user and a noise component; filtering the audio signal with a self-speech filter that utilizes an intrinsic user vector to filter out the speech component, wherein the intrinsic user vector is determined based on a voice input of the user; and outputting a filtered audio signal in which the speech component of the user has been substantially removed from the audio signal.

Machine learning based self-speech removal

Various implementations include systems for processing audio signals to remove artifacts introduced by a machine learning system in challenging environments. In particular implementations, a method includes generating a processed audio signal for a hearing assistance device in which the processed audio signal is intended to perceptually dominate a user auditory experience, including: processing an unprocessed audio signal received by the hearing assistance device, wherein the processing includes utilizing a machine learning (ML) system to generate an ML enhanced audio signal; determining a mixing coefficient from an environmental noise assessment; mixing the ML enhanced audio signal with the unprocessed audio signal using the mixing coefficient to generate the processed audio signal; and outputting the processed audio signal.

Wearable hearing assist device with artifact remediation

The technology described in this document can be embodied in a computer-implemented method that includes receiving identification information associated with a geographic location. The identification information includes one or more features that affect an acoustic environment of the geographic location at a particular time. The method also includes determining one or more parameters representing at least a subset of the one or more features, and estimating at least one acoustic parameter that represents the acoustic environment of the geographic location at the particular time. The at least one parameter can be estimated using a mapping function that generates the estimate of the at least one acoustic parameter as a weighted combination of the one or more parameters. The method further includes presenting, using a user-interface displayed on a computing device, information representing the at least one acoustic parameter estimated for the geographic location for the particular time.

Predicting acoustic features for geographic locations

Various implementations include control mechanisms for managing hearing aid usage. In some cases, an interface with a representation of the hearing aid in space is used to control audio functions in the device. In other cases, directionality of the device is controlled based upon the user's visual focus direction. In additional cases, the operating mode of the device is adjustable based upon the signature of a nearby acoustic signal.

Contextual guidance for hearing aid

Various implementations include systems for processing audio signals. In particular implementations, a system includes at least one microphone configured to capture acoustic signals; a wearable hearing assist device configured to amplify captured acoustic signals from the at least one microphone and output amplified audio signals to a transducer; a voice activity detector (VAD) configured to detect voice signals of a user from the captured acoustic signals; and a voice suppression system configured to suppress the voice signals of the user from the amplified audio signals being output to the transducer.

Hearing assist device employing dynamic processing of voice signals

An audio enhancement method includes receiving a first plurality of input signals representative of audio captured using an array of two or more sensors, the first plurality of input signals characterized by a first signal-to-noise ratio (SNR), with the audio being the signal-of-interest. The method also includes receiving a second input signal representative of the audio, the second input signal characterized by a second SNR. The second SNR is higher than the first SNR. The method further includes combining the first plurality of input signals and the second input signal to generate one or more driver signals, and driving one or more acoustic transducers using the one or more driver signals to generate an acoustic signal representative of the audio. The driver signals include spatial information derived from the first plurality of input signals, and are characterized by a third SNR that is higher than the first SNR.

Enhancement of audio from remote audio sources

Various implementations include control mechanisms for conversation assistance audio devices. In some cases, an interface with a representation of the conversation assistance audio device in space is used to control audio functions in the device. In other cases, directionality of the device is controlled based upon the user's visual focus direction. In additional cases, the operating mode of the device is adjustable based upon the signature of a nearby acoustic signal.

Conversation assistance audio device control

An audio enhancement method includes receiving a first input signal representative of audio captured using an array of two or more sensors, the first input signal characterized by a first signal-to-noise ratio (SNR), with the audio being the signal-of-interest. The method also includes receiving a second input signal representative of the audio, the second input signal characterized by a second SNR. The second SNR is higher than the first SNR. The method further includes computing a spectral mask based on a frequency domain representation of the second input signal, and processing a frequency domain representation of the first input signal based on the spectral mask to generate one or more driver signals. The method further includes driving one or more acoustic transducers using the generated driver signals.

Systems, methods, and apparatus are provided for equalization preference learning for digital audio modification. A method for listener calibration of an audio signal includes modifying a reference sound using at least one equalization curve; playing the modified reference sound for a listener; accepting listener feedback regarding the modified reference sound; and generating a weighting function based on listener feedback. A listener audio configuration system includes an output providing a sound for listener review; an interface accepting listener feedback regarding the sound; and a processor programming an audio device based on listener feedback.

Systems, methods, and apparatus for equalization preference learning

Various implementations include approaches for demonstrating wearable audio device capabilities. In particular aspects, a computer-implemented method of demonstrating a feature of a wearable audio device includes: receiving a command to initiate an audio demonstration mode at a demonstration device; initiating binaural playback of a demonstration audio file at wearable playback device being worn by a user and initiating playback of a corresponding demonstration video file at a video interface coupled with the demonstration device; receiving a user command to adjust a demonstration setting at the demonstration device to emulate adjustment of a corresponding setting on the wearable audio device; and adjusting the binaural playback at the wearable playback device based upon the user command.

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Chicago, IL, United States of America

Andrew Todd Sabin