The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics

TL;DR

This paper details the Second DiCOVA Challenge, which promotes research on COVID-19 detection through acoustic analysis of cough, speech, and breathing sounds, including dataset details, baseline system, and performance results.

Contribution

It introduces a new dataset and benchmark for acoustics-based COVID-19 detection, along with a comprehensive analysis of participant systems and their performance.

Findings

16 teams participated with diverse systems.

Baseline system provided a reference point.

Performance varied across different sound categories.

Abstract

The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the $16$ participating teams in the leaderboard is also presented.