MERIT: Learning Disentangled Music Representations for Audio Similarity

Abhinaba Roy, Junyi Liang, Dorien Herremans

MERIT: Learning Disentangled Music Representations for Audio Similarity: 5 upvotes on Hugging Face Daily Papers, #26 of 42 papers on 2026-06-03. Day-by-day upvote history.

Current music similarity models typically compute a single, monolithic score, entangling distinct musical dimensions like melody, rhythm, and timbre. This limits user control and interpretability, making it impossible to execute nuanced queries. We introduce MERIT, a framework for learning disentangled, factor-specific music representations tailored to these three core dimensions. To overcome the lack of isolated musical variations in real-world audio, we use a novel training strategy that uses conditional audio generation and source-separated stems to strongly encourage single-factor variation in training data. Our evaluations demonstrate strong factor-wise disentanglement. Each head responds strongly to its intended perceptual dimension while remaining near chance on the others, a representational property that holds across both the synthetic training domain and independent real-world audio.

Paper page on Hugging Face · arXiv

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