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LEARNING TO RANK MUSIC TRACKS USING TRIPLET LOSS

Abstract : Most music streaming services rely on automatic recommendation algorithms to exploit their large music catalogs. These algorithms aim at retrieving a ranked list of music tracks based on their similarity with a target music track. In this work, we propose a method for direct recommendation based on the audio content without explicitly tagging the music tracks. To that aim, we propose several strategies to perform triplet mining from ranked lists. We train a Convolutional Neural Network to learn the similarity via triplet loss. These different strategies are compared and validated on a large-scale experiment against an auto-tagging based approach. The results obtained highlight the efficiency of our system, especially when associated with an Auto-pooling layer.
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https://hal.telecom-paris.fr/hal-02477242
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Submitted on : Thursday, February 13, 2020 - 11:44:49 AM
Last modification on : Tuesday, October 19, 2021 - 11:16:30 AM
Long-term archiving on: : Thursday, May 14, 2020 - 2:45:17 PM

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  • HAL Id : hal-02477242, version 1
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Laure Prétet, Gael Richard, Geoffroy Peeters. LEARNING TO RANK MUSIC TRACKS USING TRIPLET LOSS. ICASSP, May 2020, Barcelona, Spain. ⟨hal-02477242⟩

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