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Learning to rank music tracks using triplet loss

Citation Author(s):
Laure Pretet, Gael Richard, Geoffroy Peeters
Submitted by:
Laure Pretet
Last updated:
18 May 2020 - 3:27am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Laure Prétet
Paper Code:
AUD-P5.4

Abstract 

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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