arXiv Open Access 2024

Benchmarking Sub-Genre Classification For Mainstage Dance Music

Hongzhi Shu Xinglin Li Hongyu Jiang Minghao Fu Xinyu Li
Lihat Sumber

Abstrak

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 sub-genres 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.

Topik & Kata Kunci

Penulis (5)

H

Hongzhi Shu

X

Xinglin Li

H

Hongyu Jiang

M

Minghao Fu

X

Xinyu Li

Format Sitasi

Shu, H., Li, X., Jiang, H., Fu, M., Li, X. (2024). Benchmarking Sub-Genre Classification For Mainstage Dance Music. https://arxiv.org/abs/2409.06690

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓