Data-driven Visual Music

Data-Driven Visual Music is a computational framework that generates synchronized audiovisual experiences by leveraging quantitative data inputs to algorithmically determine both sonic and visual parameters, distinguishing itself through empirical, data-informed mappings rather than manual composition or heuristic rules.

Publications

Citations

Authors

Institutions

Publications per year

2017–2026

Parents

Visual Music

Publications

V2Meow: Meowing to the Visual Beat via Video-to-Music Generation

Kun Su, Judith Yue Li, Qingqing Huang et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2024 · 15 citations · Full text

Humanities, Musicology, Music Analysis +14

Composing Visual Music: Visual Music Practice at the Intersection of Technology, Audio-visual Rhythms and Human Traces

Julie Watkins · Body Space & Technology · 2018 · 14 citations · Full text

Humanities, Musicology, Music Aesthetics +13

1–2 of 2

Authors

JW
Julie Watkins
University of Greenwich, United Kingdom
1 publications · 14 citations
1
h-index
TI
Timo I. Denk
Systems, Applications & Products in Data Processing (United Kingdom), United Kingdom
1 publications · 15 citations
0
h-index
MV
M. Verzetti
Deutsches Elektronen-Synchrotron DESY, Germany
1 publications · 15 citations
0
h-index
QH
Qingqing Huang
West Virginia University, United States
1 publications · 15 citations
0
h-index
CD
Chris Donahue
University of California San Diego, United States
1 publications · 15 citations
0
h-index

1–5 of 1

Institutions

1–7 of 1

Venues

Body Space & Technology
1 publications · 14 citations
1
h-index
Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 1 publications · 15 citations

1–2