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Contrastive Learning of Musical Representations
2021
Zenodo
While deep 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, contrastive learning of musical representations: CLMR. This approach works on raw time-domain music data and requires no labels to learn useful representations. We evaluate CLMR in
doi:10.5281/zenodo.5624572
fatcat:f5e5uxlbrjbsjejh56qetphhuq