  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.
