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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

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2337-2349, 2023, DOI:10.32604/csse.2023.036192

    Abstract 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, More >

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