Machine learning in acoustics: Theory and applications.

By Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, Charles-Alban Deledalle. Journal of the Acoustical Society of America, 2019

487 citations recorded by the source corpus.

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.

Subfields: Oceanography, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2e6c570d277b0b4edd48e2054d5cede4c6bbb50f

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Citations in the corpus

A Survey of Sound Source Localization with Deep Learning Methods (2021, background): “References [12] and [13] are very interesting overviews of machine learning applied to various problems in audio and acoustics.”
Deep Networks for Direction-of-Arrival Estimation in Low SNR (2020, background): “ing with a single-snapshot sample covariance matrix (SCM), which was later extended in [29], [30] to include slightly more snapshots and sources.”