Machine learning in acoustics: Theory and applications

Michael J Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A Roch, Sharon Gannot, Charles-alban Deledalle

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.