# The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

arXiv: https://arxiv.org/abs/2009.11644 · HTML: https://mlsanity.com/paper/2009.11644/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 30 of 59)

- [Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition](https://mlsanity.com/paper/2205.03433/) (2022)
- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification](https://mlsanity.com/paper/2105.07566/) (2021)
- [HeAR -- Health Acoustic Representations](https://mlsanity.com/paper/2403.02522/) (2024)
- [Lung Sound Classification Using Co-tuning and Stochastic Normalization](https://mlsanity.com/paper/2108.01991/) (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)
- [A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification](https://mlsanity.com/paper/2209.04360/) (2022)
- [ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine](https://mlsanity.com/paper/2508.14706/) (2025)
- [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)
- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [A large-scale and PCR-referenced vocal audio dataset for COVID-19](https://mlsanity.com/paper/2212.07738/) (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)
- [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)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals](https://mlsanity.com/paper/2309.05843/) (2023)
- [Cough Classification using Few-Shot Learning](https://mlsanity.com/paper/2509.09515/) (2025)
- [MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio](https://mlsanity.com/paper/2605.00969/) (2026)
- [Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice](https://mlsanity.com/paper/2112.07285/) (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)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- [Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers](https://mlsanity.com/paper/2408.15667/) (2024)

## Abstract (arXiv)

Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. However, there is currently no validated database of cough sounds with which to train such ML models. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. First, we filtered the dataset using our open-sourced cough detection algorithm. Second, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. Finally, we ensured that coughs labeled as symptomatic and COVID-19 originate from countries with high infection rates, and that their expert labels are consistent. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world's most urgent health crises.