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    Conditioned Separation of Arbitrary Sounds based on Machine Learning Models
    22.
    发明公开
    Conditioned Separation of Arbitrary Sounds based on Machine Learning Models 审中-公开

    公开(公告)号：US20230419989A1

    公开(公告)日：2023-12-28

    申请号：US17808653

    申请日：2022-06-24

    Applicant: Google LLC

    Inventor： Beat Gfeller ,  Kevin Ian Kilgour ,  Marco Tagliasacchi ,  Aren Jansen ,  Scott Thomas Wisdom ,  Qingqing Huang

    IPC: G10L25/84 ,  G10L15/16 ,  G10L15/06 ,  G06N3/04

    CPC classification number: G10L25/84 ,  G10L15/16 ,  G10L15/063 ,  G06N3/0454

    Abstract: Example methods include receiving training data comprising a plurality of audio clips and a plurality of textual descriptions of audio. The methods include generating a shared representation comprising a joint embedding. An audio embedding of a given audio clip is within a threshold distance of a text embedding of a textual description of the given audio clip. The methods include generating, based on the joint embedding, a conditioning vector and training, based on the conditioning vector, a neural network to: receive (i) an input audio waveform, and (ii) an input comprising one or more of an input textual description of a target audio source in the input audio waveform, or an audio sample of the target audio source, separate audio corresponding to the target audio source from the input audio waveform, and output the separated audio corresponding to the target audio source in response to the receiving of the input.

    Self-Supervised Audio Representation Learning for Mobile Devices
    23.
    发明申请
    Self-Supervised Audio Representation Learning for Mobile Devices 有权

    公开(公告)号：US20230085596A1

    公开(公告)日：2023-03-16

    申请号：US17986477

    申请日：2022-11-14

    Applicant: Google LLC

    Inventor： Beat Gfeller ,  Dominik Roblek ,  Félix de Chaumont Quitry ,  Marco Tagliasacchi

    IPC: G10L19/035 ,  G06N20/00 ,  G10L19/038 ,  G10L25/18

    Abstract: Systems and methods for training a machine-learned model are provided. A method can include can include obtaining an unlabeled audio signal, sampling the unlabeled audio signal to select one or more sampled slices, inputting the one or more sampled slices into a machine-learned model, receiving, as an output of the machine-learned model, one or more determined characteristics associated with the audio signal, determining a loss function for the machine-learned model based at least in part on a difference between the one or more determined characteristics and one or more corresponding ground truth characteristics of the audio signal, and training the machine-learned model from end to end based at least in part on the loss function. The one or more determined characteristics can include one or more reconstructed portions of the audio signal temporally adjacent to the one or more sampled slices or an estimated distance between two sampled slices.

    SEMI-SUPERVISED TEXT-TO-SPEECH BY GENERATING SEMANTIC AND ACOUSTIC REPRESENTATIONS
    26.
    发明申请
    SEMI-SUPERVISED TEXT-TO-SPEECH BY GENERATING SEMANTIC AND ACOUSTIC REPRESENTATIONS 有权

    公开(公告)号：US20250157456A1

    公开(公告)日：2025-05-15

    申请号：US18832325

    申请日：2024-01-26

    Applicant: Google LLC

    Inventor： Evgeny Kharitonov ,  Damien Vincent ,  Zalán Borsos ,  Raphaël Marinier ,  Olivier Claude Pietquin ,  Matthew Sharifi ,  Marco Tagliasacchi ,  Neil Zeghidour

    IPC: G10L13/027 ,  G06F40/284 ,  G06F40/30 ,  G10L13/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an audio signal from input text. In one aspect, a method comprises receiving a request to convert input text into an audio signal, wherein the input text comprises multiple tokenized text inputs, generating, using a first generative neural network, a semantic representation of the tokenized text inputs comprising semantic tokens representing semantic content of the tokenized text inputs, each semantic token being selected from a vocabulary of semantic tokens, generating, using a second generative neural network and conditioned on at least the semantic representation, an acoustic representation of the semantic representation comprising one or more respective acoustic tokens representing acoustic properties of the audio signal, and processing the acoustic representation using a decoder neural network to generate the audio signal.

    COMPRESSING AUDIO WAVEFORMS USING A STRUCTURED LATENT SPACE
    28.
    发明申请
    COMPRESSING AUDIO WAVEFORMS USING A STRUCTURED LATENT SPACE 有权

    公开(公告)号：US20250022477A1

    公开(公告)日：2025-01-16

    申请号：US18278746

    申请日：2023-03-16

    Applicant: Google LLC

    Inventor： Ahmed Omran ,  Neil Zeghidour ,  Zalán Borsos ,  Félix de Chaumont Quitry ,  Marco Tagliasacchi

    IPC: G10L19/038 ,  G10L25/30 ,  G10L25/60

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network and a decoder neural network. In one aspect, a method includes obtaining a first initial audio waveform and a first noisy audio waveform, obtaining a second initial audio waveform and a second noisy audio waveform, processing the first noisy audio waveform and the second noisy audio waveform using an encoder neural network, generating a blended embedding by concatenating: (i) clean feature dimensions from an embedding of the first noisy audio waveform, and (ii) noise feature dimensions from an embedding of the second noisy audio waveform, processing the blended embedding using a decoder neural network to generate a reconstructed audio waveform, determining gradients of an objective function; and updating parameter values of the encoder neural network and the decoder neural network using the gradients.

    Generating coded data representations using neural networks and vector quantizers
    29.
    发明授权
    Generating coded data representations using neural networks and vector quantizers 有权

    公开(公告)号：US12198710B2

    公开(公告)日：2025-01-14

    申请号：US18400992

    申请日：2023-12-29

    Applicant: Google LLC

    Inventor： Neil Zeghidour ,  Marco Tagliasacchi ,  Dominik Roblek

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

    Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media. According to one aspect, there is provided a method comprising: receiving a new input; processing the new input using an encoder neural network to generate a feature vector representing the new input; and generating a coded representation of the feature vector using a sequence of vector quantizers that are each associated with a respective codebook of code vectors, wherein the coded representation of the 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.

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