A multi-view SSL framework with combined reconstruction and separation-based contrastive losses obtains disentangled pitch and instrument subspaces without the accuracy loss seen with contrastive-only training on NSynth.
Multi-Format Contrastive Learning of Audio Representations
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abstract
Recent advances suggest the advantage of multi-modal training in comparison with single-modal methods. In contrast to this view, in our work we find that similar gain can be obtained from training with different formats of a single modality. In particular, we investigate the use of the contrastive learning framework to learn audio representations by maximizing the agreement between the raw audio and its spectral representation. We find a significant gain using this multi-format strategy against the single-format counterparts. Moreover, on the downstream AudioSet and ESC-50 classification task, our audio-only approach achieves new state-of-the-art results with a mean average precision of 0.376 and an accuracy of 90.5%, respectively.
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Balancing Information Preservation and Disentanglement in Self-Supervised Music Representation Learning
A multi-view SSL framework with combined reconstruction and separation-based contrastive losses obtains disentangled pitch and instrument subspaces without the accuracy loss seen with contrastive-only training on NSynth.