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arXiv · 2601.19109

Interpretable and Perceptually-Aligned Music Similarity with Pretrained Embeddings

Arhan Vohra·Taketo Akama
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Abstract

Perceptual similarity representations enable music retrieval systems to determine which songs sound most similar to listeners. State-of-the-art approaches based on task-specific training via self-supervised metric learning show promising alignment with human judgment, but are difficult to interpret or generalize due to limited dataset availability. We show that pretrained text-audio embeddings (CLAP and MuQ-MuLan) offer comparable perceptual alignment on similarity tasks without any additional fine-tuning. To surpass this baseline, we introduce a novel method to perceptually align pretrained embeddings with source separation and linear optimization on ABX preference data from listening tests. Our model provides interpretable and controllable instrument-wise weights, allowing music producers to retrieve stem-level loops and samples based on mixed reference songs.

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Arhan Vohra, Taketo Akama. 2026-01-27. Interpretable and Perceptually-Aligned Music Similarity with Pretrained Embeddings. https://arxiv.org/abs/2601.19109

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