Machine learning based self-speech removal

    公开(公告)号:US11750984B2

    公开(公告)日:2023-09-05

    申请号:US17032801

    申请日:2020-09-25

    CPC classification number: H04R25/45 G10L15/20 H04R2225/43

    Abstract: 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

    公开(公告)号:US20220103951A1

    公开(公告)日:2022-03-31

    申请号:US17032801

    申请日:2020-09-25

    Abstract: 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.

    Wearable hearing assist device with artifact remediation

    公开(公告)号:US11553286B2

    公开(公告)日:2023-01-10

    申请号:US17321865

    申请日:2021-05-17

    Abstract: 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.

    ENHANCEMENT OF AUDIO FROM REMOTE AUDIO SOURCES

    公开(公告)号:US20210082450A1

    公开(公告)日:2021-03-18

    申请号:US16782692

    申请日:2020-02-05

    Abstract: 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.

    WEARABLE HEARING ASSIST DEVICE WITH ARTIFACT REMEDIATION

    公开(公告)号:US20220369047A1

    公开(公告)日:2022-11-17

    申请号:US17321865

    申请日:2021-05-17

    Abstract: 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.

    Enhancement of audio from remote audio sources

    公开(公告)号:US11062723B2

    公开(公告)日:2021-07-13

    申请号:US16782692

    申请日:2020-02-05

    Abstract: 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.

    Artificial intelligence (AI) acoustic feedback suppression

    公开(公告)号:US12022268B1

    公开(公告)日:2024-06-25

    申请号:US18530669

    申请日:2023-12-06

    CPC classification number: H04R3/02 H04R3/04 H04R25/453 H04R27/00 G06N3/0464

    Abstract: 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.

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