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Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals

Bhattacharya, D and Dutta, D and Sharma, NK and Chetupalli, SR and Mote, P and Ganapathy, S and Chandrakiran, C and Nori, S and Suhail, KK and Gonuguntla, S and Alagesan, M (2022) Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals. In: 23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 18 - 22 September 2022, Incheon, pp. 2473-2477.

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Official URL: https://doi.org/10.21437/Interspeech.2022-10389


The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the variants on respiratory health of patients. We explore whether acoustic signals, collected from COVID-19 subjects, show computationally distinguishable acoustic patterns suggesting a possibility to predict the underlying virus variant. We analyze the Coswara dataset which is collected from three subject pools, namely, i) healthy, ii) COVID-19 subjects recorded during the delta variant dominant period, and iii) data from COVID-19 subjects recorded during the omicron surge. Our findings suggest that multiple sound categories, such as cough, breathing, and speech, indicate significant acoustic feature differences when comparing COVID-19 subjects with omicron and delta variants. The classification areas-under-the-curve are significantly above chance for differentiating subjects infected by omicron from those infected by delta. Using a score fusion from multiple sound categories, we obtained an area-under-the-curve of 89 and 52.4 sensitivity at 95 specificity. Additionally, a hierarchical three class approach was used to classify the acoustic data into healthy and COVID-19 positive, and further COVID-19 subjects into delta and omicron variants providing high level of 3-class classification accuracy. These results suggest new ways for designing sound based COVID-19 diagnosis approaches.

Item Type: Conference Paper
Publication: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publisher: International Speech Communication Association
Additional Information: The copyright for this article belongs to the Author(s).
Keywords: Classification (of information); Diagnosis; Speech communication, Areas under the curves; Breathing; Cough; Counting; Multiple waves; Omicron; Respiratory sounds; SARS-CoV-2 variant; Sound signal; Vowel, Coronavirus; COVID-19
Department/Centre: Others
Date Deposited: 10 Nov 2022 06:23
Last Modified: 10 Nov 2022 06:23
URI: https://eprints.iisc.ac.in/id/eprint/77858

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