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Learning music representations that are general-purpose offers theflexibility to finetune several downstream tasks using smaller datasets. Thewav2vec 2.0 speech representation model showed promising results in manydownstream speech tasks, but has been less effective when adapted to music. Inthis paper, we evaluate whether pre-training wav2vec 2.0 directly on music datacan be a better solution instead of finetuning the speech model. We illustratethat when pre-training on music data, the discrete latent representations areable to encode the semantic meaning of musical concepts such as pitch andinstrument. Our results show that finetuning wav2vec 2.0 pre-trained on musicdata allows us to achieve promising results on music classification tasks thatare competitive with prior work on audio representations. In addition, theresults are superior to the pre-trained model on speech embeddings,demonstrating that wav2vec 2.0 pre-trained on music data can be a promisingmusic representation model.

Learning Music Representations with wav2vec 2.0