The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection.

Journal: Journal of voice : official journal of the Voice Foundation
Published Date: Nov 1, 2024

Abstract

OBJECTIVES: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.

Authors

  • Zhao Ren
  • Yi Chang
  • Katrin D Bartl-Pokorny
    EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany; Division of Phoniatrics, Medical University of Graz, Graz, Austria; Division of Physiology, Medical University of Graz, Graz, Austria. Electronic address: katrin.bartl-pokorny@medunigraz.at.
  • Florian B Pokorny
    Division of Phoniatrics, Research Unit interdisciplinary Developmental Neuroscience, Medical University of Graz, 8036, Graz, Austria.
  • Björn W Schuller
    GLAM - the Group on Language, Audio, & Music, Imperial College London, London, United Kingdom.

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