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Machine learning in acoustics: Theory and applications

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Bianco, Michael J., Gerstoft, Peter, Traer, James, Ozanich, Emma, Roch, Marie A., Gannot, Sharon, Deledalle, Charles-Alban·2019·The Journal of the Acoustical Society of America
DOI 10.1121/1.5133944
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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.

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  • Machine learning techniques are revolutionising acoustic data processing through the development of automatic detection, measurement, and species-specific call recognisers.

    “However, machine learning techniques to process acoustic data are revolutionising such studies, through the development of techniques including automatic detection, measurement, and the application of species‐specific call recognisers (Bianco et al. 2019; Teixeira et al. 2019; Eichinski et al. 2022; Stowell 2022).”

    From Roars, Rumbles, and Resonance: A Systematic Review and Meta‐Analysis of Crocodylian Acoustic Signals (2026) · cites this paper as Bianco et al. 2019

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Authors
Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A.
Affiliations
  • Scripps Institution of Oceanography (US)
  • Massachusetts Institute of Technology (US)
  • San Diego State University (US)
  • Bar-Ilan University (IL)
  • University of California San Diego (US)
ORCID
Venue
The Journal of the Acoustical Society of America
Publisher
Acoustical Society of America (ASA)
Published
2019-11-01
Volume
146
Issue
5
Pages
3590-3628
ISSN
  • 0001-4966 (ISSN-L)
  • 1520-8524
DOI
10.1121/1.5133944
OpenAlex
W2989691494
Cited by
609
References
20
Citations by year
2026: 77 · 2025: 95 · 2024: 107 · 2023: 104 · 2022: 84 · 2021: 96 · 2020: 42 · 2019: 3
FWCI
35.58
Type
journal article
Language
en
Open access
Locations
  • The Journal of the Acoustical Society of America (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Office of Naval Research (N00014-18-1-2118)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
machine learning, deep learning, acoustic source localization, speech processing, ocean acoustics, bioacoustics, environmental sounds, reverberation, human speech, data-driven modeling

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