COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
Dive into the fascinating world of musical audio representations with COCOLA (Coherence-Oriented Contrastive Learning for Audio), a novel contrastive learning method that captures harmonic and rhythmic coherence in music tracks. Discover how COCOLA evaluates compositional models for accompaniment generation and introduces CompoNet, a new baseline for compositional music generation based on ControlNet. Explore the potential of COCOLA in training models on public datasets like MUSDB18-HQ, MoisesDB, Slakh2100, and CocoChorales.
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