Data-Driven Music Composition is an algorithmic composition approach that systematically generates musical structures by leveraging empirical data and statistical models to inform compositional decisions, distinguishing it from rule-based or intuition-driven methods.
43
Publications
2K
Citations
112
Authors
55
Institutions
Publications per year
2017–2026
25
Parents
Publications
43
Yu-Siang Huang, Yi‐Hsuan Yang · 2020 · 248 citations
Machine learning research that matters for music creation: A case study
Bob L. Sturm, Oded Ben‐Tal, Úna Monaghan et al. · Journal of New Music Research · 2018 · 113 citations · Full text
1–5 of 43
Authors
112
- YY
5
h-index
- BL
4
h-index
- OB
4
h-index
- YH
3
h-index
- JFJoão Felipe Santos
Institut National de la Recherche Scientifique, Canada
2 publications · 127 citations
2
h-index
1–5 of 112
Institutions
55
Queen Mary University of London
London, United Kingdom
3 h-index
4 publications
203 citations
Academia Sinica
Taipei, Taiwan
3 h-index
5 publications
634 citations
University of California San Diego
San Diego, United States
2 h-index
4 publications
49 citations
Tamkang University
Taipei, Taiwan
1 h-index
1 publications
28 citations
Durham University
Durham, United Kingdom
1 h-index
1 publications
113 citations
Association for Computing Machinery
New York, United States
1 h-index
1 publications
125 citations
UNSW Sydney
Sydney, Australia
1 h-index
1 publications
16 citations
University of Edinburgh
Edinburgh, United Kingdom
1 h-index
2 publications
278 citations
1–8 of 55
Venues
- arXiv (Cornell University)
Repository · 9 publications · 433 citations
9
h-index
- Journal of New Music Research
3 publications · 208 citations
3
h-index
- Zenodo (CERN European Organization for Nuclear Research)
Repository · 2 publications · 37 citations
2
h-index
- Computer Music Journal
2 publications · 37 citations
2
h-index
- Journal of Ambient Intelligence and Humanized Computing
1 publications · 24 citations
1
h-index
1–5