JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908366312308736 |
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| author | Roy, Abhinaba Liu, Renhang Lu, Tongyu Herremans, Dorien |
| author_facet | Roy, Abhinaba Liu, Renhang Lu, Tongyu Herremans, Dorien |
| contents | We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 362,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that leverages both musical features and metadata to identify similar songs, which are then used to fill in missing metadata using a local large language model (LLLM). This approach allows us to provide a more comprehensive and informative dataset for researchers working on music-language understanding tasks. We validate this approach quantitatively with five different measurements. By making the JamendoMaxCaps dataset publicly available, we provide a high-quality resource to advance research in music-language understanding tasks such as music retrieval, multimodal representation learning, and generative music models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata Roy, Abhinaba Liu, Renhang Lu, Tongyu Herremans, Dorien Sound Artificial Intelligence We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 362,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that leverages both musical features and metadata to identify similar songs, which are then used to fill in missing metadata using a local large language model (LLLM). This approach allows us to provide a more comprehensive and informative dataset for researchers working on music-language understanding tasks. We validate this approach quantitatively with five different measurements. By making the JamendoMaxCaps dataset publicly available, we provide a high-quality resource to advance research in music-language understanding tasks such as music retrieval, multimodal representation learning, and generative music models. |
| title | JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2502.07461 |