公开(公告)号：US20230121764A1

公开(公告)日：2023-04-20

申请号：US17502890

申请日：2021-10-15

Applicant: Lemon Inc.

Inventor： Ju-Chiang Wang ,  Jordan Smith ,  Wei Tsung Lu

IPC: G10H1/00 ,  G06N3/08

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.

公开(公告)号：US12106740B2

公开(公告)日：2024-10-01

申请号：US17502890

申请日：2021-10-15

Applicant: Lemon Inc.

Inventor： Ju-Chiang Wang ,  Jordan Smith ,  Wei Tsung Lu

IPC: G06F17/00 ,  G06N3/08 ,  G10H1/00

CPC classification number: G10H1/0008 ,  G06N3/08 ,  G10H2210/076 ,  G10H2250/311

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.

公开(公告)号：US20230386437A1

公开(公告)日：2023-11-30

申请号：US17804198

申请日：2022-05-26

Applicant: Lemon Inc.

Inventor： Ju-Chiang Wang ,  Yun-Ning Hung ,  Jordan Smith

IPC: G10H1/00 ,  G06N3/08

CPC classification number: G10H1/0008 ,  G06N3/08 ,  G10H2210/056

Abstract: System and methods directed to identifying music theory labels for audio tracks are described. More specifically, a first training set of audio portions may be generated from a plurality of audio tracks, segments within the plurality of audio tracks being labeled according to a plurality of music theory labels. A deep neural network model may then be trained using the first training set as an input, a first loss function for music theory label identifications of audio portions of the first training set, and a second loss function for segment boundary identifications within the audio portions of the first training set. In examples, the music theory label identifications and the segment boundary identifications are generated by the deep neural network model. A first audio track is received and segment boundary identifications and music theory labels for segments within the first audio track are generated using the deep neural network model.