COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations

Abstract

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).

Publication
arXiv preprint
Emilian Postolache
Emilian Postolache
PhD student

Generative Audio Maverick

Giorgio Mariani
Giorgio Mariani
PhD Student

Refactor specialist

Michele Mancusi
Michele Mancusi
PhD Student

PhD Student @SapienzaRoma CS | Intern @Musixmatch

Luca Cosmo
Luca Cosmo
Assistant Professor
Emanuele Rodolà
Emanuele Rodolà
Full Professor