The Use of Machine Learning Algorithms in the Classification of Sound

Author:

Ekpezu Akon O.1ORCID,Katsriku Ferdinand1,Yaokumah Winfred1ORCID,Wiafe Isaac1ORCID

Affiliation:

1. University of Ghana, Ghana

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.

Publisher

IGI Global

Subject

Multidisciplinary,General Engineering,General Business, Management and Accounting,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated Classification of Animal Vocalization into Estrus and Non-Estrus Condition using AI Techniques;2023 OITS International Conference on Information Technology (OCIT);2023-12-13

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