Benchmarking Sub-Genre Classification For Mainstage Dance Music
Shu, Hongzhi Li, Xinglin Jiang, Hongyu Fu, Minghao Li, Xinyu
Shu, Hongzhi
Li, Xinglin
Jiang, Hongyu
Fu, Minghao
Li, Xinyu
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Department
Machine Learning
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Music classification, a cornerstone of music information retrieval, supports a wide array of applications. To address the lack of comprehensive datasets and effective methods for sub-genre classification in mainstage dance music, we introduce a novel benchmark featuring a new dataset and baseline. Our dataset expands the scope of subgenres to reflect the diversity of recent mainstage live sets performed by leading DJs at global music festivals, capturing the vibrant and rapidly evolving electronic dance music (EDM) scene that engages millions of fans worldwide. We employ a continuous soft labeling approach to accommodate tracks blending multiple sub-genres, preserving their inherent complexity. Experiments demonstrate that even state-of-the-art multimodal large language models (MLLMs) struggle with this task, while our specialized baseline models achieve high accuracy. This benchmark supports applications such as music recommendation, DJ set curation, and interactive multimedia systems, with video demos provided. Our code and data are all open-sourced at https://github.com/Gariscat/housex-v2.git.
Citation
H. Shu, X. Li, H. Jiang, M. Fu and X. Li, "Benchmarking Sub-Genre Classification For Mainstage Dance Music," 2025 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Tahoe City, CA, USA, 2025, pp. 1-5, doi: 10.1109/WASPAA66052.2025.11231007
Source
Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Conference
2025 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2025
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Source
2025 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2025
Publisher
IEEE