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

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Abstract

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.

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Data Availability

The datasets used and analyzed in this study are described in COSWARA–A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis and available at https://doi.org/10.48550/arXiv.2005.10548 The code of this work can be made available to the readers upon request via email.

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  1. Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, DY Patil Deemed to be University, Navi Mumbai, India

    Sandeep B. Sangle & Chandrakant J. Gaikwad

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  1. Sandeep B. Sangle
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  2. Chandrakant J. Gaikwad
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Correspondence to Chandrakant J. Gaikwad.

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Sangle, S.B., Gaikwad, C.J. COVID-19 Respiratory Sound Signal Detection Using HOS-Based Linear Frequency Cepstral Coefficients and Deep Learning. Circuits Syst Signal Process 43, 331–347 (2024). https://doi.org/10.1007/s00034-023-02474-4

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