  In this work, we investigate an approach that relies on contrastive learning
and music metadata as a weak source of supervision to train music
representation models. Recent studies show that contrastive learning can be
used with editorial metadata (e.g., artist or album name) to learn audio
representations that are useful for different classification tasks. In this
paper, we extend this idea to using playlist data as a source of music
similarity information and investigate three approaches to generate anchor and
positive track pairs. We evaluate these approaches by fine-tuning the
pre-trained models for music multi-label classification tasks (genre, mood, and
instrument tagging) and music similarity. We find that creating anchor and
positive track pairs by relying on co-occurrences in playlists provides better
music similarity and competitive classification results compared to choosing
tracks from the same artist as in previous works. Additionally, our best
pre-training approach based on playlists provides superior classification
performance for most datasets.
