Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)

<Previous Article In Volume
Next Article In Volume>

COVID-19 Detection Using Audio Processing: A Systematic Literature Review

Authors
Arifa Fauziya1, *, Armein Z. R. Langi1
1Bandung Institute of Technology, Bandung, Indonesia
*Corresponding author. Email: 23522053@std.stei.itb.ac.id
Corresponding Author
Arifa Fauziya
Available Online 2 February 2024.
DOI
10.2991/978-94-6463-366-5_19How to use a DOI?
Keywords
Cough Detection; COVID-19; Systematic Literature Review
Abstract

This paper reports a systematic literature review regarding (i) datasets, (ii) processing algorithms, and (iii) their corresponding performance of cough audio processing based on COVID-19 disease detection. Early detection of respiratory diseases that is fast, practical, non-intrusive, and affordable is needed to prevent such diseases from turning into pandemics, such as in the recent COVID-19 case. We have proposed such a detection system using cough audio processing, as coughing is a recognizable sign of many respiratory illnesses, such as pulmonary edema, tuberculosis, pneumonia, whooping cough, and asthma, with future COVID-19 variants as a prime target. This study finds that the Coswara dataset is the most widely used, the Mel Frequency Cepstral Coefficient (MFCC) is the most popular extraction method, and SVM is the most common classifier. Overall, the accuracy that has been obtained is quite high, therefore the implementation of this cough detection system is convincing enough to continue. A cough detection system can then be designed to use several algorithms as plugins, capable of executing an optimal algorithm trained using a particular dataset.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
Series
Advances in Intelligent Systems Research
Publication Date
2 February 2024
ISBN
10.2991/978-94-6463-366-5_19
ISSN
1951-6851
DOI
10.2991/978-94-6463-366-5_19How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Arifa Fauziya
AU  - Armein Z. R. Langi
PY  - 2024
DA  - 2024/02/02
TI  - COVID-19 Detection Using Audio Processing: A Systematic Literature Review
BT  - Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
PB  - Atlantis Press
SP  - 201
EP  - 213
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-366-5_19
DO  - 10.2991/978-94-6463-366-5_19
ID  - Fauziya2024
ER  -