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LEARNING TO RANK MUSIC TRACKS USING TRIPLET LOSS

Laure Prétet 1, 2 Gael Richard 3 Geoffroy Peeters 4
1 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
3 Analyse et synthèse sonores [Paris]
STMS - Sciences et Technologies de la Musique et du Son
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
Contributor : Laure Prétet <>
Submitted on : Thursday, February 13, 2020 - 11:44:49 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:56 PM
Document(s) archivé(s) le : 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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