  While supervised learning has enabled great advances in many areas of music,
labeled music datasets remain especially hard, expensive and time-consuming to
create. In this work, we introduce SimCLR to the music domain and contribute a
large chain of audio data augmentations, to form a simple framework for
self-supervised learning of raw waveforms of music: CLMR. This approach
requires no manual labeling and no preprocessing of music to learn useful
representations. We evaluate CLMR in the downstream task of music
classification on the MagnaTagATune and Million Song datasets. A linear
classifier fine-tuned on representations from a pre-trained CLMR model achieves
an average precision of 35.4% on the MagnaTagATune dataset, superseding fully
supervised models that currently achieve a score of 34.9%. Moreover, we show
that CLMR's representations are transferable using out-of-domain datasets,
indicating that they capture important musical knowledge. Lastly, we show that
self-supervised pre-training allows us to learn efficiently on smaller labeled
datasets: we still achieve a score of 33.1% despite using only 259 labeled
songs during fine-tuning. To foster reproducibility and future research on
self-supervised learning in music, we publicly release the pre-trained models
and the source code of all experiments of this paper on GitHub.
