SONGFORMER: SCALING MUSIC STRUCTURE ANALYSIS WITH HETEROGENEOUS SUPERVISION

Chunbo Hao^*, Ruibin Yuan^*, Jixun Yao, Qixin Deng, Xinyi Bai, Wei Xue, Lei Xie^†

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For more information, please visit our github repository

SongFormer is a music structure analysis framework that leverages multi-resolution self-supervised representations and heterogeneous supervision, accompanied by the large-scale multilingual dataset SongFormDB and the high-quality benchmark SongFormBench to foster fair and reproducible research.

Citation

If our work and codebase is useful for you, please cite as:

@misc{hao2025songformer,
  title         = {SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision},
  author        = {Chunbo Hao and Ruibin Yuan and Jixun Yao and Qixin Deng and Xinyi Bai and Wei Xue and Lei Xie},
  year          = {2025},
  eprint        = {2510.02797},
  archivePrefix = {arXiv},
  primaryClass  = {eess.AS},
  url           = {https://arxiv.org/abs/2510.02797}
}

License

Our code is released under CC-BY-4.0 License.