Machine Learning For Acoustics
Machine Learning for Acoustics is an academic discipline that applies machine learning methodologies to the analysis, interpretation, and modeling of acoustic signals, distinguishing itself by its focus on learning patterns and structures from auditory data to solve tasks such as sound classification, source separation, and acoustic event detection.
666
Publications
29.6K
Citations
2K
Authors
711
Institutions
Publications per year
2017–2026
436
Parents
666
Christopher K. W. Tam · Annual Review of Fluid Mechanics · 1995 · 903 citations
Wave physics as an analog recurrent neural network
Tyler W. Hughes, Ian A. D. Williamson, Momchil Minkov et al. · Science Advances · 2019 · 365 citations · Full text
Natural Sciences, Physics, Acoustics +14
1–5 of 666
2K
21 publications · 1.6K citations
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h-index
13 publications · 1.1K citations
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711

University of Surrey
Guildford, United Kingdom
12 h-index
39 publications
1.7K citations

Imperial College London
London, United Kingdom
11 h-index
13 publications
882 citations

Tampere University
Tampere, Finland
10 h-index
29 publications
2.7K citations

Centre National de la Recherche Scientifique
Paris, France
10 h-index
22 publications
1.3K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
20 publications
650 citations

Marche Polytechnic University
Ancona, Italy
7 h-index
24 publications
1.1K citations

University of Passau
Passau, Germany
6 h-index
17 publications
2.7K citations
Tsinghua University
Beijing, China
6 h-index
27 publications
2.1K citations
1–8 of 711
Repository · 27 publications · 956 citations
18
h-index
JASA · 19 publications · 1.2K citations
15
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TASLP · 11 publications · 636 citations
13
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12 publications · 1.1K citations
12
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11 publications · 450 citations
12
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