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The Use of Machine Learning Algorithms in the Classification of Sound: A Systematic Review

Author

Listed:
  • Akon O. Ekpezu

    (University of Ghana, Ghana)

  • Ferdinand Katsriku

    (University of Ghana, Ghana)

  • Winfred Yaokumah

    (University of Ghana, Ghana)

  • Isaac Wiafe

    (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.

Suggested Citation

  • Akon O. Ekpezu & Ferdinand Katsriku & Winfred Yaokumah & Isaac Wiafe, 2022. "The Use of Machine Learning Algorithms in the Classification of Sound: A Systematic Review," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 13(1), pages 1-28, January.
    • Handle: RePEc:igg:jssmet:v:13:y:2022:i:1:p:1-28
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    References listed on IDEAS

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    4. Neeta Baporikar & Isaac Okoth Randa, 2020. "Organizational Design for Performance Management in State-Owned Enterprises," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 11(4), pages 1-25, October.
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