Audio Self-supervised Learning
Audio Self-Supervised Learning is a machine learning paradigm that belongs to the broader category of self-supervised learning, distinguished by its focus on learning representations from raw audio signals without human-annotated labels by designing pretext tasks that leverage the intrinsic structure of audio data.
127
Publications
8K
Citations
417
Authors
128
Institutions
Publications per year
2017–2026
125
Parents
127
Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings
Aurora Cramer, Ho-Hsiang Wu, Justin Salamon et al. · 2019 · 281 citations
Self-Supervised MultiModal Versatile Networks
Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider et al. · arXiv (Cornell University) · 2020 · 195 citations · Full text
1–5 of 127
417
9
h-index
6
h-index
5
h-index
5
h-index
5
h-index
1–5 of 417
128
Meta (Israel)
Tel Aviv, Israel
6 h-index
12 publications
1.2K citations
Google (United States)
Mountain View, United States
6 h-index
16 publications
463 citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
8 publications
476 citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
8 publications
127 citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
15 publications
642 citations
NTT (Japan)
Tokyo, Japan
4 h-index
15 publications
470 citations
Adobe Systems (United States)
San Jose, United States
4 h-index
6 publications
358 citations

National Taiwan University
Taipei, Taiwan
4 h-index
17 publications
1.3K citations
1–8 of 128
Repository · 27 publications · 3.6K citations
19
h-index
TASLP · 5 publications · 334 citations
9
h-index
Conference · 4 publications · 243 citations
5
h-index
5 publications · 89 citations
5
h-index
Conference · 4 publications · 97 citations
4
h-index
1–5