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

arXiv: https://arxiv.org/abs/2110.01177 · HTML: https://mlsanity.com/paper/2110.01177/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2021.

## Built on by (most cited first, 8 of 8)

- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model](https://mlsanity.com/paper/2406.18731/) (2024)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- [Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach](https://mlsanity.com/paper/2409.11123/) (2024)
- [ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge](https://mlsanity.com/paper/2505.18217/) (2025)
- [CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning](https://mlsanity.com/paper/2508.03764/) (2025)

## Abstract (arXiv)

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.