Starkey Laboratories, Inc.

Amazon Technologies, Inc.

Starkey Hearing Technologies

Giri, Ritwik

Zhang, Tao

Mustiere, Fred

Woods, William S.

Merk, Ivo Leon Diane Marie

Mckinney, Martin

Fitz, Kelly

Abdollahi, Mohammad

Xu, Buye

Tao Zhang

Ritwik Giri

Buye Xu

Karim Helwani

Ivo Merks

Kelly R Fitz

Jon Schmidt Kindred

William S Woods

Martin McKinney

Jinjun Xiao

Frederic Philippe Denis Mustiere

Yan Zhao

Joao Felipe Santos

Jeffrey Paul Solum

Karrie Recker

Eric Durant

Simon Carlile

Carlos Renato Calcada Nakagawa

Sidney A Higgins

Arthur Salvetti

Sahar Akram

Zhi-Quan Luo

Srikanth Vishnubhotla

Carlos Renato Nakagawa

Timothy Daniel Trine

Jerry L Yanz

Brent Edwards

Brian Lee Fideler

Wei Li Lin

Yiteng Huang

Joyce Rosenthal

Wenyu Jin

Janice LoPresti

Jumana Harianawala

Thomas Spaulding

Ivo Leon Diane Marie Merks

Mohammad Abdollahi

Matthew D Kleffner

Dean G Meyer

Mihran H Touriguian

Dominic Perz

Robert P Jacoby

Wenqiang Pu

Venkat Ramachandran

Fred Mustiere

Mohammed Reza Movahedi

Deepak Kadetotad

Mingyi Hong

Jie Ding

Mohammad Reza Movahedi

Navin Chatlani

Daniel Marquardt

Wei-Cheng Liao

Huili Chen

Yifeng Fan

Jarred Barber

Christopher Yue Ng

Sneha Hulivan Girisha

Ulrike Axen

Vishal Vijayakumar

JuanJuan Xiang

Miquel Espi Maques

Kaibao Nie

Zhiquan Luo

Miguel Espi Maques

Karrie Larae Recker

Ivo Leon Diane Marie Merk

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

A system includes a mobile device that receives an audio signal from a microphone of the mobile device. The mobile device processes the audio signal via a neural network to obtain a speech-enhanced audio signal. The system includes an ear-wearable device comprising a data interface operable to communicate with the external data interface of the mobile device. The ear-wearable device includes an audio processing path coupled to the data interface and is operable to receive the speech-enhanced audio signal and reproduce the speech-enhanced audio in an ear of a user.

Mobile device that provides sound enhancement for hearing device

A method, comprising receiving at least one sound at an electronic device. The at least one sound is enhanced for the at least one user based on a compound metric. The compound metric is calculated using at least two sound metrics selected from an engineering metric, a perceptual metric, and a physiological metric. The engineering metric comprises a difference between an output signal and a desired signal. At least one of the perceptual metric and the physiological metric is based at least in part on input sensed from the at least one user in response to the received at least one sound.

Electronic device using a compound metric for sound enhancement

An ear-wearable device stores a plurality of neural network data objects each defining a respective neural network. A sound signal received from a microphone of the ear-wearable device is digitized. An ambient environment of the digitized sound signal is classified into one of a plurality of classifications. Based on the classification, one of the neural network data objects is selected to enhance the digitized sound signal. An analog signal is formed based on the enhanced digitized sound signal. The analog signal is reproduced via a receiver of the ear-wearable device.

Hearing device with multiple neural networks for sound enhancement

In an audio signal, one or more processing circuits recognize spoken content in a user's own speech signal using speech recognition and natural language understanding. The spoken content describes a listening difficulty of the user. The one or more processing circuits generate, based on the spoken content, one or more actions for hearing devices and feedback for the user. The one or more actions attempt to resolve the listening difficulty. Additionally, the one or more processing circuits convert the user feedback to verbal feedback using speech synthesis and transmit the one or more actions and the verbal feedback to the hearing devices via a body-worn device. The hearing devices are configured to perform the one or more actions and play back the verbal feedback to the user.

Interactive system for hearing devices

A system comprises an ear-worn electronic device configured to be worn by a wearer. The ear-worn electronic device comprises a processor and memory coupled to the processor. The memory is configured to store an annoying sound dictionary representative of a plurality of annoying sounds pre-identified by the wearer. A microphone is coupled to the processor and configured to monitor an acoustic environment of the wearer. A speaker or a receiver is coupled to the processor. The processor is configured to identify different background noises present in the acoustic environment, determine which of the background noises correspond to one or more of the plurality of annoying sounds, and attenuate the one or more annoying sounds in an output signal provided to the speaker or receiver.

Ear-worn electronic device incorporating annoyance model driven selective active noise control

A subscription storage service of a provider network may be used to select a particular datastore to store a topic subscription record for an IoT device (e.g., after the IoT device subscribes to an MQTT topic). The service may select the particular datastore based on the type of the subscription (e.g., non-wildcard vs. wildcard subscription) and one or more criteria associated with the topic (e.g., subscribe operation TPS for the topic). This may allow the service to store wildcard subscriptions to a different structure and/or datastore (e.g., a 'trie' structure of an in-memory data store), while offloading the storage of non-wildcard subscriptions to another type of datastore (e.g., a key-value store) that has lower performance and/or operational cost to store data.

Data store selection and consistent routing using a pointer table

Devices and techniques are generally described for nearest device arbitration. In various examples, a first device may receive first audio data representing a wakeword spoken by a first speaker at a first time. In some examples, a second device may receive second audio data representing the wakeword spoken by the first speaker at the first time. In some cases, the first device may generate first feature data representing the first audio data and the second device may generate second feature data representing the second audio data. In various examples, a machine learning model may use the first feature data and the second feature data to generate first prediction data representing a prediction that the first device is closer to the first speaker than the second device.

Device arbitration for speech processing

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

A system may create a localized machine learning model including one or more customized local parameter values using a global model and variance data. The localized machine learning model may be used by a device or cohort of devices to perform evaluations of data. The localized model may be trained based off a global model that is adjusted and then trained a certain number of steps, where the number of steps is based at least in part on the variance data. The variance data may include variance data from other device cohorts which is received from a remote device, which can also re-train the global model using the variance data and/or the localized machine learning model(s).

Updating machine learning models across devices

A computing system includes a memory and at least one processor. The memory is configured to store motion data indicative of motion of a hearing instrument. The at least one processor is configured to determine a type of activity performed by a user of the hearing instrument and output data indicating the type of activity performed by the user.

Activity detection using a hearing instrument

A computing device is described that obtains a representation of a target ear canal of a user. Using a machine-learned model that has been trained based at least in part on representations of previously fabricated ear-wearable devices, the computing device generates a representation of an ear-wearable device for the target ear canal.

Ear-wearable device shell modeling

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Eden Prairie, MN, United States of America

Tao Zhang