COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
arxiv(2024)
摘要
We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a
contrastive learning method for musical audio representations that captures the
harmonic and rhythmic coherence between samples. Our method operates at the
level of stems (or their combinations) composing music tracks and allows the
objective evaluation of compositional models for music in the task of
accompaniment generation. We also introduce a new baseline for compositional
music generation called CompoNet, based on ControlNet ,
generalizing the tasks of MSDM, and quantify it against the latter using
COCOLA. We release all models trained on public datasets containing separate
stems (MUSDB18-HQ, MoisesDB, Slakh2100, and CocoChorales).
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