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S2Cap: A Benchmark and a Baseline for Singing Style Captioning

Computation and Language 2025-08-19 v3 Artificial Intelligence Machine Learning Sound Audio and Speech Processing

Abstract

Singing voices contain much richer information than common voices, including varied vocal and acoustic properties. However, current open-source audio-text datasets for singing voices capture only a narrow range of attributes and lack acoustic features, leading to limited utility towards downstream tasks, such as style captioning. To fill this gap, we formally define the singing style captioning task and present S2Cap, a dataset of singing voices with detailed descriptions covering diverse vocal, acoustic, and demographic characteristics. Using this dataset, we develop an efficient and straightforward baseline algorithm for singing style captioning. The dataset is available at https://zenodo.org/records/15673764.

Keywords

Cite

@article{arxiv.2409.09866,
  title  = {S2Cap: A Benchmark and a Baseline for Singing Style Captioning},
  author = {Hyunjong Ok and Jaeho Lee},
  journal= {arXiv preprint arXiv:2409.09866},
  year   = {2025}
}

Comments

CIKM 2025 Resource Paper

R2 v1 2026-06-28T18:45:24.447Z