Abstract:
This study is a systematic review of literature on the classification of sounds in three domains:
bioacoustics, biomedical acoustics, and ecoacoustics. Specifically, 68 conferences and journal
articles published between 2010 and 2019 were reviewed. The findings indicated that support vector
machines, convolutional neural networks, artificial neural networks, and statistical models were
predominantly used in sound classification across the three domains. Also, the majority of studies
that investigated medical acoustics focused on respiratory sounds analysis. Thus, it is suggested
that studies in biomedical acoustics should pay attention to the classification of other internal body
organs to enhance diagnosis of a variety of medical conditions. With regard to ecoacoustics, studies
on extreme events such as tornadoes and earthquakes for early detection and warning systems were
lacking. The review also revealed that marine and animal sound classification was dominant in
bioacoustics studies
