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

arXiv: https://arxiv.org/abs/2105.10619 · HTML: https://mlsanity.com/paper/2105.10619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2021.

## Built on by (most cited first, 1 of 1)

- [On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice](https://mlsanity.com/paper/2204.04802/) (2022)

## Abstract (arXiv)

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%.