Paper Info
Title
Enriched Music Representations With Multiple Cross-Modal Contrastive Learning
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.
Year
DOI
Venue
2021
10.1109/LSP.2021.3071082
IEEE SIGNAL PROCESSING LETTERS
Keywords
DocType
Volume
Music, Task analysis, Multiple signal classification, Training, Mood, Metadata, Recommender systems, Acoustic signal processing, Machine learning, Music information retrieval, Recommender systems
Journal
28
ISSN
Citations 
PageRank 
1070-9908
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Andres Ferraro174.64
Xavier Favory2102.08
Drossos Konstantinos35712.51
Yuntae Kim400.68
Dmitry Bogdanov523620.72