ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915772302884864 |
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| author | Sienkiewicz, Bruno Neumann, Łukasz Modrzejewski, Mateusz |
| author_facet | Sienkiewicz, Bruno Neumann, Łukasz Modrzejewski, Mateusz |
| contents | 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 21k music-caption-tags 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models Sienkiewicz, Bruno Neumann, Łukasz Modrzejewski, Mateusz Sound Artificial Intelligence Machine Learning 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 21k music-caption-tags 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. |
| title | ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models |
| topic | Sound Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.14157 |