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SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

Basanta Kumar Swain1, Mohammad Zubair Khan2,*, Chiranji Lal Chowdhary3, Abdullah Alsaeedi4

1 Department of Computer Science & Engineering, Government College of Engineering, Kalahandi, Bhawanipatna, 766002, India
2 Department of Computer Science and Information, Taibah University, Medina, 42353, Saudi Arabia
3 School of Information Technology and Engineering, VIT University Vellore, 632014, Tamil Nadu, India
4 Computer Science Department, College of Computer Science and Engineering, Taibah University, Medina, 42353, Saudi Arabia

* Corresponding Author: Mohammad Zubair Khan. Email: email

Computer Systems Science and Engineering 2023, 46(2), 2337-2349.


This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients (GFCC) for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network (DHO-ANN). The noisy crowdsourced cough datasets were collected from the public domain. This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora. The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures, F1 score, confusion matrix, specificity, and sensitivity parameters. Besides, it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset, COUGHVID. Moreover, the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5% compared to the existing methods.


Cite This Article

B. K. Swain, M. Z. Khan, C. L. Chowdhary and A. Alsaeedi, "Src: superior robustness of covid-19 detection from noisy cough data using gfcc," Computer Systems Science and Engineering, vol. 46, no.2, pp. 2337–2349, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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