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

arXiv: https://arxiv.org/abs/2103.09148 · HTML: https://mlsanity.com/paper/2103.09148/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2021.

## Built on by (most cited first, 21 of 21)

- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features](https://mlsanity.com/paper/2104.02477/) (2021)
- [Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges](https://mlsanity.com/paper/2107.14549/) (2021)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [Audio feature ranking for sound-based COVID-19 patient detection](https://mlsanity.com/paper/2104.07128/) (2021)
- [Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough](https://mlsanity.com/paper/2201.01669/) (2022)
- [A Summary of the ComParE COVID-19 Challenges](https://mlsanity.com/paper/2202.08981/) (2022)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals](https://mlsanity.com/paper/2206.12309/) (2022)
- [DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021](https://mlsanity.com/paper/2107.06126/) (2021)
- [COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection](https://mlsanity.com/paper/2206.11045/) (2022)
- [Multimodal Audio-based Disease Prediction with Transformer-based Hierarchical Fusion Network](https://mlsanity.com/paper/2410.09289/) (2024)
- [ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge](https://mlsanity.com/paper/2505.18217/) (2025)
- [EIHW-MTG DiCOVA 2021 Challenge System Report](https://mlsanity.com/paper/2110.06543/) (2021)
- [Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML)](https://mlsanity.com/paper/2201.11109/) (2022)
- [Fused Audio Instance and Representation for Respiratory Disease Detection](https://mlsanity.com/paper/2204.10581/) (2022)
- [Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021](https://mlsanity.com/paper/2206.05462/) (2022)
- [EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection](https://mlsanity.com/paper/2309.05357/) (2023)
- [Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition](https://mlsanity.com/paper/2501.12050/) (2025)
- [Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example](https://mlsanity.com/paper/2508.08892/) (2025)
- [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026)

## Abstract (arXiv)

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.