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    End-to-end time-domain multitask learning for ML-based speech enhancement
    1.
    发明授权
    End-to-end time-domain multitask learning for ML-based speech enhancement 有权

    公开(公告)号：US11996114B2

    公开(公告)日：2024-05-28

    申请号：US17321411

    申请日：2021-05-15

    Applicant: Apple Inc.

    Inventor： Ramin Pishehvar ,  Ante Jukic ,  Mehrez Souden ,  Jason Wung ,  Feipeng Li ,  Joshua D. Atkins

    IPC: G10L15/16 ,  G06N20/00 ,  G10L21/0216

    CPC classification number: G10L21/0216 ,  G06N20/00 ,  G10L15/16 ,  G10L2021/02166

    Abstract: Disclosed is a multi-task machine learning model such as a time-domain deep neural network (DNN) that jointly generate an enhanced target speech signal and target audio parameters from a mixed signal of target speech and interference signal. The DNN may encode the mixed signal, determine masks used to jointly estimate the target signal and the target audio parameters based on the encoded mixed signal, apply the mask to separate the target speech from the interference signal to jointly estimate the target signal and the target audio parameters, and decode the masked features to enhance the target speech signal and to estimate the target audio parameters. The target audio parameters may include a voice activity detection (VAD) flag of the target speech. The DNN may leverage multi-channel audio signal and multi-modal signals such as video signals of the target speaker to improve the robustness of the enhanced target speech signal.

    Echo cancellation using a subset of multiple microphones as reference channels
    6.
    发明授权
    Echo cancellation using a subset of multiple microphones as reference channels 有权

    公开(公告)号：US10978086B2

    公开(公告)日：2021-04-13

    申请号：US16517400

    申请日：2019-07-19

    Applicant: Apple Inc.

    Inventor： Jason Wung ,  Sarmad Aziz Malik ,  Ashrith Deshpande ,  Ante Jukic ,  Joshua D. Atkins

    IPC: G10L21/0208 ,  G10K11/178 ,  G10L21/0216

    Abstract: An echo canceller is disclosed in which audio signals of the playback content received by one or more of the microphones from a loudspeaker of the device may be used as the playback reference signals to estimate the echo signals of the playback content received by a target microphone for echo cancellation. The echo canceller may estimate the transfer function between a reference microphone and the target microphone based on the playback reference signal of the reference microphone and the signal of the target microphone. To mitigate near-end speech cancellation at the target microphone, the echo canceller may compute a mask to distinguish between target microphone audio signals that are echo-signal dominant and near-end speech dominant. The echo canceller may use the mask to adaptively update the transfer function or to modify the playback reference signal used by the transfer function to estimate the echo signals of the playback content.

    Echo Cancellation Using A Subset of Multiple Microphones As Reference Channels
    7.
    发明申请
    Echo Cancellation Using A Subset of Multiple Microphones As Reference Channels 有权

    公开(公告)号：US20210020188A1

    公开(公告)日：2021-01-21

    申请号：US16517400

    申请日：2019-07-19

    Applicant: Apple Inc.

    Inventor： Jason Wung ,  Sarmad Aziz Malik ,  Ashrith Deshpande ,  Ante Jukic ,  Joshua D. Atkins

    IPC: G10L21/0208 ,  G10K11/178

    Abstract: An echo canceller is disclosed in which audio signals of the playback content received by one or more of the microphones from a loudspeaker of the device may be used as the playback reference signals to estimate the echo signals of the playback content received by a target microphone for echo cancellation. The echo canceller may estimate the transfer function between a reference microphone and the target microphone based on the playback reference signal of the reference microphone and the signal of the target microphone. To mitigate near-end speech cancellation at the target microphone, the echo canceller may compute a mask to distinguish between target microphone audio signals that are echo-signal dominant and near-end speech dominant. The echo canceller may use the mask to adaptively update the transfer function or to modify the playback reference signal used by the transfer function to estimate the echo signals of the playback content.

    Microphone array based deep learning for time-domain speech signal extraction
    8.
    发明授权
    Microphone array based deep learning for time-domain speech signal extraction 有权

    公开(公告)号：US11508388B1

    公开(公告)日：2022-11-22

    申请号：US17100802

    申请日：2020-11-20

    Applicant: Apple Inc.

    Inventor： Mehrez Souden ,  Symeon Delikaris Manias ,  Joshua D. Atkins ,  Ante Jukic ,  Ramin Pishehvar

    IPC: G10L21/0232 ,  H04R1/40 ,  G10L25/30 ,  G06N3/08 ,  H04R3/00 ,  G10L21/0216

    Abstract: A device for processing audio signals in a time-domain includes a processor configured to receive multiple audio signals corresponding to respective microphones of at least two or more microphones of the device, at least one of the multiple audio signals comprising speech of a user of the device. The processor is configured to provide the multiple audio signals to a machine learning model, the machine learning model having been trained based at least in part on an expected position of the user of the device and expected positions of the respective microphones on the device. The processor is configured to provide an audio signal that is enhanced with respect to the speech of the user relative to the multiple audio signals, wherein the audio signal is a waveform output from the machine learning model.

    End-To-End Time-Domain Multitask Learning for ML-Based Speech Enhancement
    9.
    发明申请
    End-To-End Time-Domain Multitask Learning for ML-Based Speech Enhancement 有权

    公开(公告)号：US20220366927A1

    公开(公告)日：2022-11-17

    申请号：US17321411

    申请日：2021-05-15

    Applicant: Apple Inc.

    Inventor： Ramin Pishehvar ,  Ante Jukic ,  Mehrez Souden ,  Jason Wung ,  Feipeng Li ,  Joshua D. Atkins

    IPC: G10L21/0216 ,  G10L15/16 ,  G06N20/00

    Abstract: Disclosed is a multi-task machine learning model such as a time-domain deep neural network (DNN) that jointly generate an enhanced target speech signal and target audio parameters from a mixed signal of target speech and interference signal. The DNN may encode the mixed signal, determine masks used to jointly estimate the target signal and the target audio parameters based on the encoded mixed signal, apply the mask to separate the target speech from the interference signal to jointly estimate the target signal and the target audio parameters, and decode the masked features to enhance the target speech signal and to estimate the target audio parameters. The target audio parameters may include a voice activity detection (VAD) flag of the target speech. The DNN may leverage multi-channel audio signal and multi-modal signals such as video signals of the target speaker to improve the robustness of the enhanced target speech signal.

    Learning-based distance estimation
    10.
    发明授权
    Learning-based distance estimation 有权

    公开(公告)号：US11222652B2

    公开(公告)日：2022-01-11

    申请号：US16516780

    申请日：2019-07-19

    Applicant: Apple Inc.

    Inventor： Ante Jukic ,  Mehrez Souden ,  Joshua D. Atkins

    IPC: G10L25/51 ,  G10L15/22 ,  G10L15/16 ,  G10L25/78 ,  G10L15/06 ,  H04R3/00 ,  G10L15/08 ,  H04R1/40

    Abstract: A learning based system such as a deep neural network (DNN) is disclosed to estimate a distance from a device to a speech source. The deep learning system may estimate the distance of the speech source at each time frame based on speech signals received by a compact microphone array. Supervised deep learning may be used to learn the effect of the acoustic environment on the non-linear mapping between the speech signals and the distance using multi-channel training data. The deep learning system may estimate the direct speech component that contains information about the direct signal propagation from the speech source to the microphone array and the reverberant speech signal that contains the reverberation effect and noise. The deep learning system may extract signal characteristics of the direct signal component and the reverberant signal component and estimate the distance based on the extracted signal characteristics using the learned mapping.

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