A Dataset of Amateur Karaoke Singing and Audiobook Narration

Georgieva E, Ripollés P, McFee B. A Dataset of Amateur Karaoke Singing and Audiobook Narration. In: 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio. Audio Engineering Society; 2025. Paper 19. Available from: https://aes.org/publications/elibrary-page/?id=23008

@inproceedings{Georgieva2025_23008,
  author = {Georgieva, Elena and Ripollés, Pablo and McFee, Brian},
  title = {{A Dataset of Amateur Karaoke Singing and Audiobook Narration}},
  booktitle = {2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio},
  note = {Paper 19},
  year = {2025},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=23008}
}

TY  - CPAPER
TI  - A Dataset of Amateur Karaoke Singing and Audiobook Narration
AU  - Georgieva, Elena
AU  - Ripollés, Pablo
AU  - McFee, Brian
T2  - 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio
M1  - Paper 19
PY  - 2025
DA  - 2025/09/02
UR  - https://aes.org/publications/elibrary-page/?id=23008
PB  - Audio Engineering Society
LA  - en
AB  - Singing and narration are fundamental forms of vocal expression. Listener-evaluated data is essential for developing AI systems that better capture the nuances of vocal expression and resonate with listeners perceptually and emotionally. In this paper, we present a dataset with 4300 ratings of 940 recordings of amateur karaoke singing and audiobook narration, annotated by 86 participants. Participants rated multiple aspects of audio quality and vocal performance including skill, likability, passion, sincerity, and intelligibility, among others. We release this dataset alongside baseline analyses comparing ratings of singing and audiobook narration, as well as ratings of clean excerpts versus those with added degradations. We also release a linear regression model that estimates listener ratings from audio. This dataset will serve as a valuable resource for future research in music and speech, with applications in intelligent music production, vocal audio effect recommendation, audiobook editing, content personalization, and automated assessment of singing and speaking voices, to name a few.
ER  -