# Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds

arXiv: https://arxiv.org/abs/2009.08790 · HTML: https://mlsanity.com/paper/2009.08790/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition](https://mlsanity.com/paper/2205.03433/) (2022)
- [Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification](https://mlsanity.com/paper/2105.07566/) (2021)
- [A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels](https://mlsanity.com/paper/2111.05895/) (2021)
- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)
- [Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19](https://mlsanity.com/paper/2103.01806/) (2021)
- [Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough](https://mlsanity.com/paper/2107.10716/) (2021)
- [Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data](https://mlsanity.com/paper/2104.02005/) (2021)
- [Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges](https://mlsanity.com/paper/2107.14549/) (2021)
- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [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)
- [Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough](https://mlsanity.com/paper/2201.01669/) (2022)
- [TB or not TB? Acoustic cough analysis for tuberculosis classification](https://mlsanity.com/paper/2209.00934/) (2022)
- [End-2-End COVID-19 Detection from Breath & Cough Audio](https://mlsanity.com/paper/2102.08359/) (2021)
- [Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds](https://mlsanity.com/paper/2106.12174/) (2021)
- [TinyM2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices](https://mlsanity.com/paper/2202.04303/) (2022)
- [A Summary of the ComParE COVID-19 Challenges](https://mlsanity.com/paper/2202.08981/) (2022)
- [COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection](https://mlsanity.com/paper/2206.11045/) (2022)
- [Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Example](https://mlsanity.com/paper/2504.00730/) (2025)
- [Impact of data-splits on generalization: Identifying COVID-19 from cough and context](https://mlsanity.com/paper/2106.03851/) (2021)
- [An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification](https://mlsanity.com/paper/2110.00508/) (2021)
- [On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice](https://mlsanity.com/paper/2204.04802/) (2022)
- [EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection](https://mlsanity.com/paper/2309.05357/) (2023)
- [Benchmarking AI for low-resource contexts: Thinking beyond leaderboards](https://mlsanity.com/paper/2605.28508/) (2026)

## Abstract (arXiv)

Testing capacity for COVID-19 remains a challenge globally due to the lack of adequate supplies, trained personnel, and sample-processing equipment. These problems are even more acute in rural and underdeveloped regions. We demonstrate that solicited-cough sounds collected over a phone, when analysed by our AI model, have statistically significant signal indicative of COVID-19 status (AUC 0.72, t-test,p <0.01,95% CI 0.61-0.83). This holds true for asymptomatic patients as well. Towards this, we collect the largest known(to date) dataset of microbiologically confirmed COVID-19 cough sounds from 3,621 individuals. When used in a triaging step within an overall testing protocol, by enabling risk-stratification of individuals before confirmatory tests, our tool can increase the testing capacity of a healthcare system by 43% at disease prevalence of 5%, without additional supplies, trained personnel, or physical infrastructure