COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations

Understand

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 the stems composing music tracks and can input features obtained via Harmonic-Percussive Separation (HPS).
• COCOLA allows the objective evaluation of generative models for music accompaniment generation, which are difficult to benchmark with established metrics.
• In this regard, we evaluate recent music accompaniment generation models, demonstrating the effectiveness of the proposed method.