Bose Corporation

Li-Chia Yang

Marko Stamenovic

Jahn Dmitri Eichfeld

Daniel Ross Tengelsen

Carl Ralph Jensen

Kenneth S Lyons

Sara Maree Adkins

Andrew Todd Sabin

Kenneth Scott Lyons

Various implementations include audio processing system having artificial intelligence (AI) acoustic feedback suppression. In some particular aspects, an audio processing system includes: an input adapted to receive an acoustic signal via a microphone; an electroacoustic transducer; an amplifier configured to amplify the acoustic signal and output an amplified signal via the electroacoustic transducer; and an artificial intelligence (AI) system having a machine learning model that processes the acoustic signal prior to amplification to produce a dynamic filter, wherein the AI system applies the dynamic filter to the acoustic signal to suppress feedback in the amplified signal.

Artificial intelligence (AI) acoustic feedback suppression

Various implementations include systems for processing audio signals. In particular implementations, a system for processing audio signals includes: an accessory device that includes a first processor for running a machine learning model on an input signal, where the machine learning model includes a classifier configured to generate metadata associated with the input signal; and a wearable audio device configured for wireless communication with the accessory device, the wearable audio device including a second processor that utilizes the metadata from the accessory device to process a source audio signal and output a processed audio signal.

Audio processing using distributed machine learning model

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

Various implementations include systems for processing audio signals. In particular implementations, a system for processing audio signals includes: a wearable audio device having a transducer and a communication system; and an accessory device configured to wirelessly communicate with the wearable audio device, the accessory device having a processor configured to process a source audio signal according to a method that includes: source separating the source audio signal; and providing a source separated audio signal to the wearable audio device for transduction.

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Waltham, MA, United States of America

Li-Chia Yang