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arXiv · 2105.10619

COVID-19 Detection Using Recorded Coughs in the 2021 DiCOVA Challenge

Benjamin Elizalde·Daniel Tompkins
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Abstract

COVID-19 has resulted in over 100 million infections and caused worldwide lock downs due to its high transmission rate and limited testing options. Current diagnostic tests can be expensive, limited in availability, time-intensive and require risky in-person appointments. It has been established that symptomatic COVID-19 seriously impairs normal functioning of the respiratory system, thus affecting the coughing acoustics. The 2021 DiCOVA Challenge @ INTERSPEECH was designed to find scientific and engineering insights to the question by enabling participants to analyze an acoustic dataset gathered from COVID-19 positive and non-COVID-19 individuals. In this report we describe our participation in the Challenge (Track 1). We achieved 82.37% AUC ROC on the blind test outperforming the Challenge's baseline of 69.85%.

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Benjamin Elizalde, Daniel Tompkins. 2021-05-22. COVID-19 Detection Using Recorded Coughs in the 2021 DiCOVA Challenge. https://arxiv.org/abs/2105.10619

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