  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.
