Learning to rank music tracks using triplet loss

TL;DR

This paper introduces a CNN-based method using triplet loss for music track recommendation directly from audio content, outperforming auto-tagging approaches in large-scale experiments.

Contribution

It presents novel triplet mining strategies for training a neural network to learn music similarity without explicit tagging.

Findings

System achieves high recommendation accuracy

Auto-pooling layer improves performance

Outperforms auto-tagging based methods

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.