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    Compressing audio waveforms using neural networks and vector quantizers
    13.
    发明授权
    Compressing audio waveforms using neural networks and vector quantizers 有权

    公开(公告)号：US11600282B2

    公开(公告)日：2023-03-07

    申请号：US17856856

    申请日：2022-07-01

    Applicant: Google LLC

    Inventor： Neil Zeghidour ,  Marco Tagliasacchi ,  Dominik Roblek

    IPC: G10L19/038 ,  G10L25/30 ,  G10L19/00 ,  G06N3/08 ,  G06N3/04

    Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media. One of the methods includes receiving an audio waveform that includes a respective audio sample for each of a plurality of time steps, processing the audio waveform using an encoder neural network to generate a plurality of feature vectors representing the audio waveform, generating a respective coded representation of each of the plurality of feature vectors using a plurality of vector quantizers that are each associated with a respective codebook of code vectors, wherein the respective coded representation of each feature vector identifies a plurality of code vectors, including a respective code vector from the codebook of each vector quantizer, that define a quantized representation of the feature vector, and generating a compressed representation of the audio waveform by compressing the respective coded representation of each of the plurality of feature vectors.

    Learning Strides in Convolutional Neural Networks
    14.
    发明申请
    Learning Strides in Convolutional Neural Networks 有权

    公开(公告)号：US20250005354A1

    公开(公告)日：2025-01-02

    申请号：US18698691

    申请日：2022-10-05

    Applicant: Google LLC

    Inventor： Neil Zeghidour ,  Rachid Riad ,  Olivier Teboul ,  David Grangier

    IPC: G06N3/08

    Abstract: A method of training a machine learning model, includes receiving training data for the machine learning model, wherein the training data comprises a plurality of batches. The method also includes applying a downsampling layer of the machine learning model to the plurality of batches of the training data to determine a stride comprising a learnable parameter for the downsampling layer. Applying the downsampling layer of the machine learning model to a batch of the training data includes projecting an input in a spatial domain to a Fourier domain, constructing a mask in the Fourier domain based on a current value of the stride and dimensions of the input, applying the mask as a low-pass filter to the projected input to produce a tensor in the Fourier domain, cropping the tensor based on the mask, and transforming the cropped tensor to the spatial domain.

    Compressing audio waveforms using neural networks and vector quantizers
    15.
    发明授权
    Compressing audio waveforms using neural networks and vector quantizers 有权

    公开(公告)号：US11990148B2

    公开(公告)日：2024-05-21

    申请号：US18106094

    申请日：2023-02-06

    Applicant: Google LLC

    Inventor： Neil Zeghidour ,  Marco Tagliasacchi ,  Dominik Roblek

    IPC: G10L19/038 ,  G06N3/045 ,  G06N3/08 ,  G10L19/00 ,  G10L25/30

    CPC classification number: G10L19/038 ,  G06N3/045 ,  G06N3/08 ,  G10L25/30 ,  G10L2019/0002

    Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media. One of the methods includes receiving an audio waveform that includes a respective audio sample for each of a plurality of time steps, processing the audio waveform using an encoder neural network to generate a plurality of feature vectors representing the audio waveform, generating a respective coded representation of each of the plurality of feature vectors using a plurality of vector quantizers that are each associated with a respective codebook of code vectors, wherein the respective coded representation of each feature vector identifies a plurality of code vectors, including a respective code vector from the codebook of each vector quantizer, that define a quantized representation of the feature vector, and generating a compressed representation of the audio waveform by compressing the respective coded representation of each of the plurality of feature vectors.

    End-to-end speech diarization via iterative speaker embedding
    17.
    发明授权
    End-to-end speech diarization via iterative speaker embedding 有权

    公开(公告)号：US11887623B2

    公开(公告)日：2024-01-30

    申请号：US17304514

    申请日：2021-06-22

    Applicant: Google LLC

    Inventor： David Grangier ,  Neil Zeghidour ,  Oliver Teboul

    IPC: G10L25/78 ,  G06N3/04 ,  G10L15/06 ,  G10L15/07 ,  G10L17/18 ,  G10L19/008

    CPC classification number: G10L25/78 ,  G06N3/04 ,  G10L15/063 ,  G10L15/07 ,  G10L17/18 ,  G10L19/008

    Abstract: A method includes receiving an input audio signal corresponding to utterances spoken by multiple speakers. The method also includes encoding the input audio signal into a sequence of T temporal embeddings. During each of a plurality of iterations each corresponding to a respective speaker of the multiple speakers, the method includes selecting a respective speaker embedding for the respective speaker by determining a probability that the corresponding temporal embedding includes a presence of voice activity by a single new speaker for which a speaker embedding was not previously selected during a previous iteration and selecting the respective speaker embedding for the respective speaker as the temporal embedding. The method also includes, at each time step, predicting a respective voice activity indicator for each respective speaker of the multiple speakers based on the respective speaker embeddings selected during the plurality of iterations and the temporal embedding.

    SEPARATING SPEECH BY SOURCE IN AUDIO RECORDINGS BY PREDICTING ISOLATED AUDIO SIGNALS CONDITIONED ON SPEAKER REPRESENTATIONS
    19.
    发明申请
    SEPARATING SPEECH BY SOURCE IN AUDIO RECORDINGS BY PREDICTING ISOLATED AUDIO SIGNALS CONDITIONED ON SPEAKER REPRESENTATIONS 有权

    公开(公告)号：US20230112265A1

    公开(公告)日：2023-04-13

    申请号：US17967726

    申请日：2022-10-17

    Applicant: Google LLC

    Inventor： Neil Zeghidour ,  David Grangier

    IPC: G10L21/028 ,  G06N3/08 ,  G10L17/04 ,  G10L17/18 ,  G10L21/0316 ,  G06N3/045

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing speech separation. One of the methods includes obtaining a recording comprising speech from a plurality of speakers; processing the recording using a speaker neural network having speaker parameter values and configured to process the recording in accordance with the speaker parameter values to generate a plurality of per-recording speaker representations, each speaker representation representing features of a respective identified speaker in the recording; and processing the per-recording speaker representations and the recording using a separation neural network having separation parameter values and configured to process the recording and the speaker representations in accordance with the separation parameter values to generate, for each speaker representation, a respective predicted isolated audio signal that corresponds to speech of one of the speakers in the recording.

    COMPRESSING AUDIO WAVEFORMS USING NEURAL NETWORKS AND VECTOR QUANTIZERS
    20.
    发明申请
    COMPRESSING AUDIO WAVEFORMS USING NEURAL NETWORKS AND VECTOR QUANTIZERS 有权

    公开(公告)号：US20230019128A1

    公开(公告)日：2023-01-19

    申请号：US17856856

    申请日：2022-07-01

    Applicant: Google LLC

    Inventor： Neil Zeghidour ,  Marco Tagliasacchi ,  Dominik Roblek

    IPC: G10L19/038 ,  G10L25/30 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media. One of the methods includes receiving an audio waveform that includes a respective audio sample for each of a plurality of time steps, processing the audio waveform using an encoder neural network to generate a plurality of feature vectors representing the audio waveform, generating a respective coded representation of each of the plurality of feature vectors using a plurality of vector quantizers that are each associated with a respective codebook of code vectors, wherein the respective coded representation of each feature vector identifies a plurality of code vectors, including a respective code vector from the codebook of each vector quantizer, that define a quantized representation of the feature vector, and generating a compressed representation of the audio waveform by compressing the respective coded representation of each of the plurality of feature vectors.

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