COVID-19 Respiratory Sound Signal Detection Using HOS-Based Linear Frequency Cepstral Coefficients and Deep Learning

COVID-19 virus has become a very critical human health hazard. Many variants are reported, and still, the virus is mutating. Thus, we get new strains now and then. COVID-19 detection at an early stage is an important issue that will help in the efficient management of the disease. This work studies...

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Veröffentlicht in:Circuits, systems, and signal processing systems, and signal processing, 2024, Vol.43 (1), p.331-347
Hauptverfasser: Sangle, Sandeep B., Gaikwad, Chandrakant J.
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Sprache:eng
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Zusammenfassung:COVID-19 virus has become a very critical human health hazard. Many variants are reported, and still, the virus is mutating. Thus, we get new strains now and then. COVID-19 detection at an early stage is an important issue that will help in the efficient management of the disease. This work studies COVID-19 audio signals originating from breathing, coughing, and vowel sounds. In the literature, most of the works on this topic use MFCC-based features. In this work, various methods are proposed for COVID-19 detection. The proposed methods use accumulated bispectrum features that capture the distinctive properties of COVID-19 in the above signals. Three new methods are proposed for COVID-19 detection. The performance of the presented methods is analyzed in detail, and comparison with the state-of-the-art methods is given. For various signals, considerable performance improvement is seen in the proposed methods. The CNN and ResNet-50 network models are used in this study.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-023-02474-4