DiCOVA challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics

Muguli, A and Pinto, L and Nirmala, R and Sharma, N and Krishnan, P and Ghoshy, PK and Kumar, R and Bhat, S and Chetupalli, SR and Ganapathy, S and Ramoji, S and Nanda, V (2021) DiCOVA challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics. In: 22nd Annual Conference of the International Speech Communication Association, 30 - 3 September 2021, Brno, pp. 4241-4245.

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Abstract

The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning. This challenge is an open call for researchers to analyze a dataset of sound recordings, collected from COVID-19 infected and non-COVID-19 individuals, for a two-class classification. These recordings were collected via crowdsourcing from multiple countries, through a website application. The challenge features two tracks, one focusing on cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings. In this paper, we introduce the challenge and provide a detailed description of the task, and present a baseline system for the task.

Item Type:	Conference Proceedings
Publication:	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Series.:	Audio and Speech Processing (eess.AS)
Publisher:	International Speech Communication Association
Additional Information:	The copyright for this article belongs to the International Speech Communication Association.
Keywords:	Acoustics; COVID-19; Healthcare; Machine learning; Respiratory diagnosis
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	28 Oct 2023 02:56
Last Modified:	28 Oct 2023 02:56
URI:	https://eprints.iisc.ac.in/id/eprint/82779

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