Enriched Music Representations with Multiple Cross-modal Contrastive Learning

TL;DR

This paper introduces a novel cross-modal contrastive learning method that combines multiple music-related data sources to improve music representation quality, outperforming baseline models across several tasks.

Contribution

It presents a new approach that integrates heterogeneous music data sources using contrastive learning to enhance music representations.

Findings

Outperforms baseline audio CNN in all tasks

Achieves comparable performance to state-of-the-art methods

Highlights importance of multiple data sources in training

Abstract

Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio, interactions between users and songs, or associated genre metadata. Recently, contrastive learning has led to representations that generalize better compared to traditional supervised methods. In this paper, we present a novel approach that combines multiple types of information related to music using cross-modal contrastive learning, allowing us to learn an audio feature from heterogeneous data simultaneously. We align the latent representations obtained from playlists-track interactions, genre metadata, and the tracks' audio, by maximizing the agreement between these modality representations using a contrastive loss. We evaluate our approach in three tasks, namely, genre classification, playlist continuation and automatic tagging. We compare the performances with a baseline audio-based CNN trained to predict these modalities. We also study the importance of including multiple sources of information when training our embedding model. The results suggest that the proposed method outperforms the baseline in all the three downstream tasks and achieves comparable performance to the state-of-the-art.