META-LEARNING FOR ADAPTIVE FILTERS
    1.
    发明公开

    公开(公告)号:US20230343350A1

    公开(公告)日:2023-10-26

    申请号:US18155611

    申请日:2023-01-17

    Applicant: Adobe Inc.

    CPC classification number: G10L21/0232 G10L25/30 G10L25/18 G10L2021/02082

    Abstract: Embodiments are disclosed for performing a using a neural network to optimize filter weights of an adaptive filter. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving, by a filter, an input audio signal, wherein the input audio signal is a far-end audio signal, the filter including a transfer function with adaptable filter weights, generating a response audio signal modeling the input audio signal passing through the acoustic environment, receiving a target response signal, including the input audio signal and near-end audio signals, calculating an adaptive filter loss, generating, by a trained recurrent neural network, a filter weight update using the calculated adaptive filter loss, updating the adaptable filter weights of the transfer function to create an updated transfer function, generating an updated response audio signal based on the updated transfer function, and providing the updated response audio signal as an output audio signal.

    GENERAL-PURPOSE NEURAL AUDIO FINGERPRINTING
    2.
    发明公开

    公开(公告)号:US20240273355A1

    公开(公告)日:2024-08-15

    申请号:US18168236

    申请日:2023-02-13

    Applicant: Adobe Inc.

    CPC classification number: G06N3/08 G06F16/632

    Abstract: Embodiments are disclosed for identifying matching content using neural content fingerprints. The method may include receiving a request to identify content matching a query content item, wherein the query content item is a time varying content item, generating, by an embedding network, a neural fingerprint for the query content item, identifying one or more candidate content items based on the neural fingerprint of the query content item, determining, by a ranking network, one or more similarity scores corresponding to the one or more candidate content items, and identifying one or more matching content items based on the one or more similarity scores.

    MULTI-LEVEL AUDIO SEGMENTATION USING DEEP EMBEDDINGS

    公开(公告)号:US20230115212A1

    公开(公告)日:2023-04-13

    申请号:US17742313

    申请日:2022-05-11

    Applicant: Adobe Inc.

    Abstract: Embodiments are disclosed for generating an audio segmentation of an audio sequence using deep embeddings. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and extracting features for each frame of the audio sequence, where each frame is associated with a beat of the audio sequence. The method may further comprise clustering frames of the audio sequence into one or more clusters based on the extracted features and generating segments of the audio sequence based on the clustered frames, where each segment includes frames of the audio sequence from a same cluster. The method may further comprise constructing a multi-level audio segmentation of the audio sequence and performing a segment fusioning process that merges shorter segments with neighboring segments based on cluster assignments.

    DETECTING AND CLASSIFYING FILLER WORDS IN AUDIO USING NEURAL NETWORKS

    公开(公告)号:US20240161735A1

    公开(公告)日:2024-05-16

    申请号:US18055739

    申请日:2022-11-15

    Applicant: Adobe Inc.

    CPC classification number: G10L15/16 G10L15/22 G10L25/78

    Abstract: Embodiments are disclosed for performing a filler word detection process on input audio by a media editing system using trained neural networks. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence, analyzing the audio sequence to determine filler word candidates, classifying, by a filler word classification model, each filler word candidate of the filler word candidates into one of a set of categories, and generating an output audio sequence, the output audio sequence including an identification of a subset of the filler word candidates in a filler words category of the set of categories as identified filler words.

    SECTION-BASED MUSIC SIMILARITY SEARCHING

    公开(公告)号:US20230129350A1

    公开(公告)日:2023-04-27

    申请号:US17742318

    申请日:2022-05-11

    Applicant: Adobe Inc.

    Abstract: Embodiments are disclosed for performing a section-based, within-song music similarity search by an audio recommendation system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and a request to determine similar audio sequences to the audio sequence from a pre-processed audio catalog, analyzing the audio sequence to generate an audio embedding for the audio sequence, querying a pre-processed audio catalog to retrieve audio embeddings for catalog audio sequences at different time resolutions, generating a set of candidate audio sequences from the pre-processed audio catalog based on the audio embedding for the audio sequence, and providing the set of candidate audio sequences.

    DEEP ENCODER FOR PERFORMING AUDIO PROCESSING

    公开(公告)号:US20220328025A1

    公开(公告)日:2022-10-13

    申请号:US17228357

    申请日:2021-04-12

    Applicant: Adobe Inc.

    Abstract: Embodiments are disclosed for determining an answer to a query associated with a graphical representation of data. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an unprocessed audio sequence and a request to perform an audio signal processing effect on the unprocessed audio sequence. The one or more embodiments further include analyzing, by a deep encoder, the unprocessed audio sequence to determine parameters for processing the unprocessed audio sequence. The one or more embodiments further include sending the unprocessed audio sequence and the parameters to one or more audio signal processing effects plugins to perform the requested audio signal processing effect using the parameters and outputting a processed audio sequence after processing of the unprocessed audio sequence using the parameters of the one or more audio signal processing effects plugins.

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