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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2511.15270 |
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| _version_ | 1866918216498937856 |
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| author | Tan, Chih-Pin Kao, Hsuan-Kai Su, Li Yang, Yi-Hsuan |
| author_facet | Tan, Chih-Pin Kao, Hsuan-Kai Su, Li Yang, Yi-Hsuan |
| contents | Recent advances in AI-based music generation have focused heavily on text-conditioned models, with less attention given to reference-based generation such as song adaptation. To support this line of research, we introduce LargeSHS, a large-scale dataset derived from SecondHandSongs, containing over 1.7 million metadata entries and approximately 900k publicly accessible audio links. Unlike existing datasets, LargeSHS includes structured adaptation relationships between musical works, enabling the construction of adaptation trees and performance clusters that represent cover song families. We provide comprehensive statistics and comparisons with existing datasets, highlighting the unique scale and richness of LargeSHS. This dataset paves the way for new research in cover song generation, reference-based music generation, and adaptation-aware MIR tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15270 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LargeSHS: A large-scale dataset of music adaptation Tan, Chih-Pin Kao, Hsuan-Kai Su, Li Yang, Yi-Hsuan Sound Recent advances in AI-based music generation have focused heavily on text-conditioned models, with less attention given to reference-based generation such as song adaptation. To support this line of research, we introduce LargeSHS, a large-scale dataset derived from SecondHandSongs, containing over 1.7 million metadata entries and approximately 900k publicly accessible audio links. Unlike existing datasets, LargeSHS includes structured adaptation relationships between musical works, enabling the construction of adaptation trees and performance clusters that represent cover song families. We provide comprehensive statistics and comparisons with existing datasets, highlighting the unique scale and richness of LargeSHS. This dataset paves the way for new research in cover song generation, reference-based music generation, and adaptation-aware MIR tasks. |
| title | LargeSHS: A large-scale dataset of music adaptation |
| topic | Sound |
| url | https://arxiv.org/abs/2511.15270 |