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Machine learning in acoustics: Theory and applications

TitleMachine learning in acoustics: Theory and applications
Publication TypeJournal Article
Year of Publication2019
AuthorsBianco M.J, Gerstoft P, Traer J., Ozanich E., Roch M.A, Gannot S., Deledalle C.A
Volume146
Pagination3590-3628
Date Published2019/11
Type of ArticleArticle
ISBN Number0001-4966
Accession NumberWOS:000510232400050
Keywordsacoustics; Audiology & Speech-Language Pathology; expectation-maximization algorithm; neural-network; noise-reduction; nonnegative matrix factorization; online dereverberation; sound-speed; source localization; source separation; speech dereverberation; wave-guide invariant
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. (C) 2019 Acoustical Society of America.

DOI10.1121/1.5133944
Student Publication: 
No
Research Topics: