Audio-Visual Separation of On-Screen Sounds based on Machine Learning Models

    公开(公告)号:US20230386502A1

    公开(公告)日:2023-11-30

    申请号:US18226545

    申请日:2023-07-26

    Applicant: Google LLC

    CPC classification number: G10L25/57 G06N3/088 G10L25/30 G06V20/40 G06F18/214

    Abstract: Apparatus and methods related to separation of audio sources are provided. The method includes receiving an audio waveform associated with a plurality of video frames. The method includes estimating, by a neural network, one or more audio sources associated with the plurality of video frames. The method includes generating, by the neural network, one or more audio embeddings corresponding to the one or more estimated audio sources. The method includes determining, based on the audio embeddings and a video embedding, whether one or more audio sources of the one or more estimated audio sources correspond to objects in the plurality of video frames. The method includes predicting, by the neural network and based on the one or more audio embeddings and the video embedding, a version of the audio waveform comprising audio sources that correspond to objects in the plurality of video frames.

    Audio-Visual Separation of On-Screen Sounds Based on Machine Learning Models

    公开(公告)号:US20220310113A1

    公开(公告)日:2022-09-29

    申请号:US17214186

    申请日:2021-03-26

    Applicant: Google LLC

    Abstract: Apparatus and methods related to separation of audio sources are provided. The method includes receiving an audio waveform associated with a plurality of video frames. The method includes estimating, by a neural network, one or more audio sources associated with the plurality of video frames. The method includes generating, by the neural network, one or more audio embeddings corresponding to the one or more estimated audio sources. The method includes determining, based on the audio embeddings and a video embedding, whether one or more audio sources of the one or more estimated audio sources correspond to objects in the plurality of video frames. The method includes predicting, by the neural network and based on the one or more audio embeddings and the video embedding, a version of the audio waveform comprising audio sources that correspond to objects in the plurality of video frames.

    Audio-visual separation of on-screen sounds based on machine learning models

    公开(公告)号:US12217768B2

    公开(公告)日:2025-02-04

    申请号:US18226545

    申请日:2023-07-26

    Applicant: Google LLC

    Abstract: Apparatus and methods related to separation of audio sources are provided. The method includes receiving an audio waveform associated with a plurality of video frames. The method includes estimating, by a neural network, one or more audio sources associated with the plurality of video frames. The method includes generating, by the neural network, one or more audio embeddings corresponding to the one or more estimated audio sources. The method includes determining, based on the audio embeddings and a video embedding, whether one or more audio sources of the one or more estimated audio sources correspond to objects in the plurality of video frames. The method includes predicting, by the neural network and based on the one or more audio embeddings and the video embedding, a version of the audio waveform comprising audio sources that correspond to objects in the plurality of video frames.

    Audio-visual separation of on-screen sounds based on machine learning models

    公开(公告)号:US11756570B2

    公开(公告)日:2023-09-12

    申请号:US17214186

    申请日:2021-03-26

    Applicant: Google LLC

    CPC classification number: G10L25/57 G06F18/214 G06N3/088 G06V20/40 G10L25/30

    Abstract: Apparatus and methods related to separation of audio sources are provided. The method includes receiving an audio waveform associated with a plurality of video frames. The method includes estimating, by a neural network, one or more audio sources associated with the plurality of video frames. The method includes generating, by the neural network, one or more audio embeddings corresponding to the one or more estimated audio sources. The method includes determining, based on the audio embeddings and a video embedding, whether one or more audio sources of the one or more estimated audio sources correspond to objects in the plurality of video frames. The method includes predicting, by the neural network and based on the one or more audio embeddings and the video embedding, a version of the audio waveform comprising audio sources that correspond to objects in the plurality of video frames.

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