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

TL;DR

The COUGHVID dataset offers a large, diverse collection of over 20,000 crowdsourced cough recordings, expertly labeled to facilitate machine learning research for respiratory disease diagnosis, including COVID-19 detection.

Contribution

This paper introduces one of the largest expert-labeled cough datasets, combining crowdsourcing and clinical validation for improved cough analysis algorithms.

Findings

Over 20,000 cough recordings collected and filtered

More than 2,000 recordings labeled by pulmonologists

Dataset includes diverse demographics and COVID-19 statuses

Abstract

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.