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公开(公告)号：US20220277729A1
公开(公告)日：2022-09-01
申请号：US17186490
申请日：2021-02-26
Applicant: Lemon Inc.
Inventor： Keunwoo Choi
IPC: G10L15/00 , G10L25/18 , G10L21/14 , G10L15/06 , G10L25/81 , G10L15/22 , G06F16/68 , G06N20/00 , G06N5/04
Abstract: The present disclosure describes techniques for identifying languages associated with music. Training data may be received, wherein the training data comprise information indicative of audio data representative of a plurality of music samples and metadata associated with the plurality of music samples. The training data further comprises information indicating a language corresponding to each of the plurality of music samples. A machine learning model may be trained to identify a language associated with a piece of music by applying the training data to the machine model until the model reaches a predetermined recognition accuracy. A language associated with the piece of music may be determined using the trained machine learning model.
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公开(公告)号：US11854558B2
公开(公告)日：2023-12-26
申请号：US17502863
申请日：2021-10-15
Applicant: Lemon Inc.
Inventor： Wei Tsung Lu , Ju-Chiang Wang , Minz Won , Keunwoo Choi , Xuchen Song
Abstract: Devices, systems and methods related to causing an apparatus to generate music information of audio data using a transformer-based neural network model with a multilevel transformer for audio analysis, using a spectral and a temporal transformer, are disclosed herein. The processor generates a time-frequency representation of obtained audio data to be applied as input for a transformer-based neural network model; determines spectral embeddings and first temporal embeddings of the audio data based on the time-frequency representation of the audio data; determines each vector of a second frequency class token (FCT) by passing each vector of the first FCT in the spectral embeddings through the spectral transformer; determines second temporal embeddings by adding a linear projection of the second FCT to the first temporal embeddings; determines third temporal embeddings by passing the second temporal embeddings through the temporal transformer; and generates music information based on the third temporal embeddings.
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Patent Agency Ranking
公开(公告)号：US12272341B2
公开(公告)日：2025-04-08
申请号：US17521435
申请日：2021-11-08
Applicant: LEMON INC.
Inventor： Zhihao Ouyang , Keunwoo Choi
Abstract: The present disclosure describes techniques for controllable music generation. The techniques comprise extracting latent vectors from unlabelled data, the unlabelled data comprising a plurality of music note sequences, the plurality of music note sequences indicating a plurality of pieces of music; clustering the latent vectors into a plurality of classes corresponding to a plurality of music styles; generating a plurality of labelled latent vectors corresponding to the plurality of music styles, each of the plurality labelled latent vectors comprising information indicating features of a corresponding music style; and generating a first music note sequence indicating a first piece of music in a particular music style among the plurality of music styles based at least in part on a particular labelled latent vector among the plurality of labelled latent vectors, the particular labelled latent vector corresponding to the particular music style.
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公开(公告)号：US12026198B2
公开(公告)日：2024-07-02
申请号：US17384576
申请日：2021-07-23
Applicant: LEMON INC.
Inventor： Minz Won , Keunwoo Choi , Yuanjian Feng
CPC classification number: G06F16/683 , G06F16/65 , G06N3/08 , G10G1/00 , G10H1/0025
Abstract: The present disclosure describes techniques for identifying music attributes. The described techniques comprises receiving audio data of a piece of music; determining at least one attribute of the piece of music based on the audio data of the piece of music using a model; the model comprising a convolutional neural network and a transformer; the model being pre-trained using training data, wherein the training data comprise labelled data associated with a first plurality of music samples and unlabelled data associated with a second plurality of music samples, the labelled data comprise audio data of the first plurality of music samples and label information indicative of attributes of the first plurality of music samples, and the unlabelled data comprise audio data of the second plurality of music samples.
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公开(公告)号：US11955110B2
公开(公告)日：2024-04-09
申请号：US17186490
申请日：2021-02-26
Applicant: LEMON INC.
Inventor： Keunwoo Choi
IPC: G10L15/00 , G06F16/68 , G06F40/263 , G06N5/04 , G06N20/00 , G10L15/06 , G10L15/22 , G10L21/14 , G10L25/18 , G10L25/81
CPC classification number: G10L15/005 , G06F16/686 , G06F40/263 , G06N5/04 , G06N20/00 , G10L15/063 , G10L15/22 , G10L21/14 , G10L25/18 , G10L25/81
Abstract: The present disclosure describes techniques for identifying languages associated with music. Training data may be received, wherein the training data comprise information indicative of audio data representative of a plurality of music samples and metadata associated with the plurality of music samples. The training data further comprises information indicating a language corresponding to each of the plurality of music samples. A machine learning model may be trained to identify a language associated with a piece of music by applying the training data to the machine model until the model reaches a predetermined recognition accuracy. A language associated with the piece of music may be determined using the trained machine learning model.
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公开(公告)号：US20230147185A1
公开(公告)日：2023-05-11
申请号：US17521435
申请日：2021-11-08
Applicant: LEMON INC.
Inventor： Zhihao Ouyang , Keunwoo Choi
CPC classification number: G10H1/0025 , G06N20/00 , G10H2210/036 , G10H2210/115 , G10H2210/571 , G10H2250/005
Abstract: The present disclosure describes techniques for controllable music generation. The techniques comprise extracting latent vectors from unlabelled data, the unlabelled data comprising a plurality of music note sequences, the plurality of music note sequences indicating a plurality of pieces of music; clustering the latent vectors into a plurality of classes corresponding to a plurality of music styles; generating a plurality of labelled latent vectors corresponding to the plurality of music styles, each of the plurality labelled latent vectors comprising information indicating features of a corresponding music style; and generating a first music note sequence indicating a first piece of music in a particular music style among the plurality of music styles based at least in part on a particular labelled latent vector among the plurality of labelled latent vectors, the particular labelled latent vector corresponding to the particular music style.
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公开(公告)号：US20230124006A1
公开(公告)日：2023-04-20
申请号：US17502863
申请日：2021-10-15
Applicant: Lemon Inc.
Inventor： Wei Tsung Lu , Ju-Chiang Wang , Minz Won , Keunwoo Choi , Xuchen Song
Abstract: Devices, systems and methods related to causing an apparatus to generate music information of audio data using a transformer-based neural network model with a multilevel transformer for audio analysis, using a spectral and a temporal transformer, are disclosed herein. The processor generates a time-frequency representation of obtained audio data to be applied as input for a transformer-based neural network model; determines spectral embeddings and first temporal embeddings of the audio data based on the time-frequency representation of the audio data; determines each vector of a second frequency class token (FCT) by passing each vector of the first FCT in the spectral embeddings through the spectral transformer; determines second temporal embeddings by adding a linear projection of the second FCT to the first temporal embeddings; determines third temporal embeddings by passing the second temporal embeddings through the temporal transformer; and generates music information based on the third temporal embeddings.
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