Machine learning in acoustics: Theory and applications
Scripps Institution of Oceanography · University of California San Diego · +3 more institutions
Abstract
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex…
Citation impact
- FWCI
- 39.17
- Percentile
- 100%
- References
- 263
Authors
7- MJMichael J. BiancoCorresponding
Scripps Institution of Oceanography, University of California San Diego
- PGPeter Gerstoft
Scripps Institution of Oceanography, University of California San Diego
- JTJames Traer
Massachusetts Institute of Technology
- EOEmma Ozanich
Scripps Institution of Oceanography, University of California San Diego
- MAMarie A. Roch
San Diego State University
Topics & keywords
- Transformative learning
- Field (mathematics)
- SIGNAL (programming language)
- Ranging
- Underwater acoustics
- Training (meteorology)