Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915795862290432 |
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| author | Baranes, Marion Hennequin, Romain Epure, Elena V. |
| author_facet | Baranes, Marion Hennequin, Romain Epure, Elena V. |
| contents | Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors but struggle with context-dependent ones. This work can further serve as a benchmark for fine-grained modeling of user intent and for gaining insights into improving LLM-based music understanding systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12301 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries Baranes, Marion Hennequin, Romain Epure, Elena V. Sound Computation and Language Information Retrieval Machine Learning Audio and Speech Processing Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors but struggle with context-dependent ones. This work can further serve as a benchmark for fine-grained modeling of user intent and for gaining insights into improving LLM-based music understanding systems. |
| title | Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries |
| topic | Sound Computation and Language Information Retrieval Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2602.12301 |