Journal article
Machine learning in acoustics: Theory and applications
The Journal of the Acoustical Society of America, Vol.146(5), pp.3590-3628
11/2019
DOI: 10.1121/1.5133944
PMID: 31795641
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 acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.
Details
- Title: Subtitle
- Machine learning in acoustics: Theory and applications
- Creators
- Michael J Bianco - Scripps Institution of Oceanography, University of California San DiegoPeter Gerstoft - Scripps Institution of Oceanography, University of California San DiegoJames Traer - Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyEmma Ozanich - Scripps Institution of Oceanography, University of California San DiegoMarie A Roch - Department of Computer Science, San Diego State UniversitySharon Gannot - Faculty of Engineering, Bar-Ilan UniversityCharles-Alban Deledalle - Department of Electrical and Computer Engineering, University of California San Diego
- Resource Type
- Journal article
- Publication Details
- The Journal of the Acoustical Society of America, Vol.146(5), pp.3590-3628
- DOI
- 10.1121/1.5133944
- PMID
- 31795641
- NLM abbreviation
- J Acoust Soc Am
- ISSN
- 0001-4966
- eISSN
- 1520-8524
- Publisher
- American Institute of Physics
- Number of pages
- 39
- Grant note
- N00014-18-1-2118 / Office of Naval Research
- Date published
- 11/2019
- Academic Unit
- Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984065368102771
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