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

TL;DR

The DiCOVA challenge promotes research on COVID-19 diagnosis through acoustic analysis of cough and speech sounds, providing a dataset, task framework, and baseline system to facilitate advancements in this interdisciplinary field.

Contribution

This paper introduces the DiCOVA challenge, including its dataset, task design, and baseline system, to advance acoustic-based COVID-19 detection research.

Findings

Dataset collected from multiple countries via crowdsourcing

Two task tracks: cough sounds and speech recordings

Baseline system established for future comparison

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.