ConceptCaps -- a Distilled Concept Dataset for Interpretability in Music Models

• URL: http://arxiv.org/abs/2601.14157v1
• Date: Tue, 20 Jan 2026 17:04:08 GMT
• Title: ConceptCaps -- a Distilled Concept Dataset for Interpretability in Music Models
• Authors: Bruno Sienkiewicz, Łukasz Neumann, Mateusz Modrzejewski,
• Abstract summary: ConceptCaps is a dataset of 23k music-caption-audio triplets with explicit labels from a 200-attribute taxonomy.<n>A VAE learns plausible attribute co-occurrence patterns, a fine-tuned LLM converts attribute lists into professional descriptions, and MusicGen synthesizes corresponding audio.<n>We validate the dataset through audio-text alignment (CLAP), linguistic quality metrics (BERTScore, MAUVE), and TCAV analysis confirming that concept probes recover musically meaningful patterns.
• Score: 0.10923877073891443
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Concept-based interpretability methods like TCAV require clean, well-separated positive and negative examples for each concept. Existing music datasets lack this structure: tags are sparse, noisy, or ill-defined. We introduce ConceptCaps, a dataset of 23k music-caption-audio triplets with explicit labels from a 200-attribute taxonomy. Our pipeline separates semantic modeling from text generation: a VAE learns plausible attribute co-occurrence patterns, a fine-tuned LLM converts attribute lists into professional descriptions, and MusicGen synthesizes corresponding audio. This separation improves coherence and controllability over end-to-end approaches. We validate the dataset through audio-text alignment (CLAP), linguistic quality metrics (BERTScore, MAUVE), and TCAV analysis confirming that concept probes recover musically meaningful patterns. Dataset and code are available online.

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