An Experimental Comparison Of Multi-view Self-supervised Methods For
  Music Tagging
Gabriel Meseguer-Brocal Dorian Desblancs Romain Hennequin
Self-supervised learning has emerged as a powerful way to pre-train
generalizable machine learning models on large amounts of unlabeled data. It is
particularly compelling in the music domain, where obtaining labeled data is
time-consuming, error-prone, and ambiguous. During the self-supervised process,
models are trained on pretext tasks, with the primary objective of acquiring
robust and informative features that can later be fine-tuned for specific
downstream tasks. The choice of the pretext task is critical as it guides the
model to shape the feature space with meaningful constraints for information
encoding. In the context of music, most works have relied on contrastive
learning or masking techniques. In this study, we expand the scope of pretext
tasks applied to music by investigating and comparing the performance of new
self-supervised methods for music tagging. We open-source a simple ResNet model
trained on a diverse catalog of millions of tracks. Our results demonstrate
that, although most of these pre-training methods result in similar downstream
results, contrastive learning consistently results in better downstream
performance compared to other self-supervised pre-training methods. This holds
true in a limited-data downstream context.