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

• Published in: Circuits, systems, and signal processing Vol. 43; no. 1; pp. 331 - 347
• Main Authors: Sangle, Sandeep B., Gaikwad, Chandrakant J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 2024; Springer Nature B.V
• Subjects: Audio signals; Circuits and Systems; COVID-19; Electrical Engineering; Electronics and Microelectronics; Engineering; Health hazards; Instrumentation; Signal detection; Signal,Image and Speech Processing; Viral diseases; Viruses; COVID-19; LFCC; CNN; Bispectrum; MFCC
• ISSN: 0278-081X; 1531-5878
• DOI: 10.1007/s00034-023-02474-4

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.