CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916454541033472 |
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| author | Piekarzewicz, Aleksandra Sroka, Tomasz Tym, Aleksander Modrzejewski, Mateusz |
| author_facet | Piekarzewicz, Aleksandra Sroka, Tomasz Tym, Aleksander Modrzejewski, Mateusz |
| contents | In this paper, we introduce CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts. We describe the selected qualities of our dataset, along with three example tasks possible to perform using this dataset: hook detection, contextual tagging and artist identification. We conduct baseline experiments and provide initial benchmarks for these tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19540 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music Piekarzewicz, Aleksandra Sroka, Tomasz Tym, Aleksander Modrzejewski, Mateusz Sound Artificial Intelligence Machine Learning Audio and Speech Processing In this paper, we introduce CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts. We describe the selected qualities of our dataset, along with three example tasks possible to perform using this dataset: hook detection, contextual tagging and artist identification. We conduct baseline experiments and provide initial benchmarks for these tasks. |
| title | CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2410.19540 |