SUPERVISED METRIC LEARNING FOR MUSIC STRUCTURE FEATURES

    公开(公告)号:WO2023063881A2

    公开(公告)日:2023-04-20

    申请号:PCT/SG2022/050705

    申请日:2022-09-29

    Applicant: LEMON INC.

    Abstract: Devices, systems, and methods related to implementing supervised metric learning during a training of a deep neural network model are disclosed herein. In examples, audio input may be received, where the audio input includes a plurality of song fragments from a plurality of songs. For each song fragment, an aligning function may be performed to center the song fragment based on determined beat information, thereby creating a plurality of aligned song fragments. For each song fragment of the plurality of song fragments, an embedding vector may be obtained from the deep neural network. Thus, a batch of aligned song fragments from the plurality of aligned song fragments may be selected, such that a training tuple may be selected. A loss metric may be generated based on the selected training tuple and one or more weights of the deep neural network model may be updated based on the loss metric.

    VIDEO REMIXING METHOD
    3.
    发明公开

    公开(公告)号:EP4099326A1

    公开(公告)日:2022-12-07

    申请号:EP21177649.7

    申请日:2021-06-03

    Applicant: Lemon Inc.

    Abstract: The present invention relates to method for generating a video remix, the method comprising: receiving an input video (101); selecting at least one excerpt from the input video (103), wherein an audio signal of the selected excerpt includes at least one onset; determining a plurality of sub-sequences of the at least one excerpt (104); and rearranging the plurality of sub-sequences according to a predetermined pattern (105) to form the video remix.

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