A cough-based Covid-19 detection with gammatone and mel-frequency cepstral coefficients
In: Diagnostyka, Jg. 24 (2023), Heft 2, S. 1-16
Online
academicJournal
Zugriff:
Many countries have adopted a public health approach that aims to address the particular challenges faced during the pandemic Coronavirus disease 2019 (COVID-19). Researchers mobilized to manage and limit the spread of the virus, and multiple artificial intelligence-based systems are designed to automatically detect the disease. Among these systems, voice-based ones since the virus have a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we distinguished positive COVID patients from healthy controls. After the gammatone cepstral coefficients (GTCC) and the Mel-frequency cepstral coefficients (MFCC) extraction, we have done the feature selection (FS) and classification with multiple machine learning algorithms. By combining all features and the 3-nearest neighbor (3NN) classifier, we achieved the highest classification results. The model is able to detect COVID-19 patients with accuracy and an f1-score above 98 percent. When applying FS, the higher accuracy and F1-score were achieved by the same model and the ReliefF algorithm, we lose 1 percent of accuracy by mapping only 12 features instead of the original 53.
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A cough-based Covid-19 detection with gammatone and mel-frequency cepstral coefficients
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Autor/in / Beteiligte Person: | Benmalek, Elmehdi ; Elmhamdi, Jamal ; Jilbab, Abdelilah ; Jbari, Atman |
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Zeitschrift: | Diagnostyka, Jg. 24 (2023), Heft 2, S. 1-16 |
Veröffentlichung: | Polskie Towarzystwo Diagnostyki Technicznej, 2023 |
Medientyp: | academicJournal |
ISSN: | 2449-5220 (print) |
DOI: | 10.29354/diag/166330 |
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