Music Representation Learning
Music Representation Learning is a computational framework within algorithmic composition that systematically acquires and structures symbolic or audio-based musical features through machine learning methods, distinguishing itself by enabling high-level musical understanding and generation through data-driven latent representations.
530
Publications
29.9K
Citations
1.2K
Authors
388
Institutions
Publications per year
2017–2026
315
Parents
530
WaveNet: A Generative Model for Raw Audio
Aäron van den Oord, Sander Dieleman, Heiga Zen et al. · arXiv (Cornell University) · 2016 · 3.6K citations · Full text
Computing, Musicology, Acoustics +16
WaveNet: A Generative Model for Raw Audio
Aäron van den Oord, Sander Dieleman, Heiga Zen et al. · arXiv (Cornell University) · 2016 · 2.5K citations · Full text
Computing, Musicology, Acoustics +16
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388
Queen Mary University of London
London, United Kingdom
18 h-index
55 publications
3.3K citations
Academia Sinica
Taipei, Taiwan
15 h-index
35 publications
3.7K citations

Johannes Kepler University of Linz
Linz, Austria
14 h-index
39 publications
925 citations
Google (United States)
Mountain View, United States
14 h-index
62 publications
5.8K citations
New York University
New York, United States
13 h-index
27 publications
1.5K citations

Centre National de la Recherche Scientifique
Paris, France
11 h-index
25 publications
825 citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
17 publications
853 citations

Télécom Paris
Palaiseau, France
11 h-index
24 publications
826 citations
1–8 of 388
Repository · 107 publications · 12.1K citations
38
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Repository · 30 publications · 1.1K citations
18
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14 publications · 639 citations
13
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TISMIR · 8 publications · 170 citations
8
h-index
8 publications · 216 citations
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