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    EFFICIENT EMBEDDING FOR ACOUSTIC MODELS
    1.
    发明公开
    EFFICIENT EMBEDDING FOR ACOUSTIC MODELS 审中-公开

    公开(公告)号：US20230360641A1

    公开(公告)日：2023-11-09

    申请号：US17736959

    申请日：2022-05-04

    申请人： Apple Inc.

    发明人： Daniel C. KLINGLER ,  Carlos M. AVENDANO ,  Jonathan HUANG ,  Miquel ESPI MARQUES

    IPC分类号： G10L15/08 ,  G10L25/78 ,  G10L25/51 ,  G06N20/20

    CPC分类号： G10L15/08 ,  G10L25/78 ,  G10L25/51 ,  G06N20/20

    摘要： The subject disclosure provides systems and methods for generating and storing learned embeddings of audio inputs to an electronic device. The electronic device may generate and store encoded versions of audio inputs and learned embeddings of the audio inputs. When a new audio input is obtained, the electronic device can generate an encoded version of the new audio input, compare the encoded version of the new audio input to the stored encoded versions of prior audio inputs, and if the encoded version of the new audio input matches one of the stored encoded versions of the prior audio inputs, the electronic device can provide a stored learned embedding that corresponds to the one of the stored encoded versions of the prior audio inputs to a detection model at the electronic device. The cached embeddings can be provided to locally trained models for detecting individual sounds using electronic devices.

    SYSTEMS AND METHODS FOR IDENTIFYING AN ACOUSTIC SOURCE BASED ON OBSERVED SOUND
    4.
    发明公开
    SYSTEMS AND METHODS FOR IDENTIFYING AN ACOUSTIC SOURCE BASED ON OBSERVED SOUND 审中-公开

    公开(公告)号：US20230177942A1

    公开(公告)日：2023-06-08

    申请号：US18103486

    申请日：2023-01-30

    申请人： Apple Inc.

    发明人： Hyung-Suk KIM ,  Daniel C. KLINGLER ,  Miquel ESPI MARQUES ,  Carlos M. AVENDANO

    IPC分类号： G08B21/18 ,  G01H3/00 ,  G08B7/06 ,  G10L25/51 ,  G06N20/00

    CPC分类号： G08B21/182 ,  G01H3/005 ,  G08B7/06 ,  G10L25/51 ,  G06N20/00

    摘要： An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

    SOUND DETECTION FOR ELECTRONIC DEVICES
    5.
    发明申请
    SOUND DETECTION FOR ELECTRONIC DEVICES 有权

    公开(公告)号：US20220391758A1

    公开(公告)日：2022-12-08

    申请号：US17737017

    申请日：2022-05-04

    申请人： Apple Inc.

    发明人： Jonathan HUANG ,  Miquel ESPI MARQUES ,  Carlos M. AVENDANO ,  Kevin M. DURAND ,  David FINDLAY ,  Vasudha KOWTHA ,  Daniel C. KLINGLER ,  Yichi ZHANG

    IPC分类号： G06N20/00 ,  G06F3/16 ,  H04R1/08 ,  G08B21/18

    摘要： The subject disclosure provides systems and methods for providing locally trained models for detecting individual sounds using electronic devices. Local detection of individual sounds with a detection model at an electronic device can be provided by obtaining training samples for the detection model with the electronic device, and generating additional negative and positive training samples based on the obtained training samples. A two-stage detection process may be provided, in which a trigger model at a device compares an audio input to a reference sound to trigger a detection model at the device. The detection of individual sounds with a detection model at an electronic device can also leverage audio capture capabilities of multiple devices in an acoustic scene to capture multiple concurrent training samples.