Paper ID: 2411.02711

Self-Supervised Multi-View Learning for Disentangled Music Audio Representations

Julia Wilkins, Sivan Ding, Magdalena Fuentes, Juan Pablo Bello

Self-supervised learning (SSL) offers a powerful way to learn robust, generalizable representations without labeled data. In music, where labeled data is scarce, existing SSL methods typically use generated supervision and multi-view redundancy to create pretext tasks. However, these approaches often produce entangled representations and lose view-specific information. We propose a novel self-supervised multi-view learning framework for audio designed to incentivize separation between private and shared representation spaces. A case study on audio disentanglement in a controlled setting demonstrates the effectiveness of our method.

Submitted: Nov 5, 2024