Evaluating Contrastive Methodologies for Music Representation Learning Using Playlist Data

Recent research shows that weakly supervised contrastive pre-training holds significant promise in learning improved representations of musical audio. Several such works use metadata (e.g. artist names or genre tags) or consumption data (e.g. playlists or user listening history) for cross-modal supervision in their representation learning pipeline. However, methodological differences inhibit direct comparison of the results reported in these studies. In this work, we implement six of these contrastive pre-training regimes under a common framework based around playlist data, benchmarking against from-scratch and self-supervised baselines and systematically evaluating performance in downstream music tagging and playlist continuation. Furthermore, we combine existing methods to produce a novel hybrid approach which shows consistently strong performance by leveraging multiple data modes. Finally, we demonstrate that mixup data augmentation, previously only used in the self-supervised scenario, also has significant downstream benefits in weakly supervised music representation learning.