MERIT: Learning Disentangled Music Representations for Audio Similarity
Summary
MERIT is a framework that learns disentangled music representations for melody, rhythm, and timbre using conditional audio generation and source-separated stems, enabling nuanced and factor-specific audio similarity queries.
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Paper page - MERIT: Learning Disentangled Music Representations for Audio Similarity
Source: https://huggingface.co/papers/2605.27346
Abstract
MERIT framework learns disentangled music representations for melody, rhythm, and timbre through conditional audio generation and source-separated stems, enabling nuanced musical queries.
Currentmusic similarity modelstypically 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 representationstailored to these three core dimensions. To overcome the lack of isolated musical variations in real-world audio, we use a novel training strategy that usesconditional audio generationandsource-separated stemsto 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.
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