10.5281/zenodo.4048312
https://zenodo.org/records/4048312
oai:zenodo.org:4048312
Lara Orlandic
Lara Orlandic
0000-0002-4078-7528
EPFL
Tomas Teijeiro
Tomas Teijeiro
0000-0002-2175-7382
EPFL
David Atienza
David Atienza
0000-0001-9536-4947
EPFL
The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
Zenodo
2020
COVID-19
cough sound database
automatic cough classification
2020-09-24
eng
10.5281/zenodo.4048311
https://zenodo.org/communities/deephealth
https://zenodo.org/communities/covid-19
https://zenodo.org/communities/epfl
https://zenodo.org/communities/embeddedsystemslab-epfl
https://zenodo.org/communities/eu
1.0
Creative Commons Attribution 4.0 International
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. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. Furthermore, 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. 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.
For more information about the data collection, pre-processing, validation, and data structure, please refer to the following publication: https://arxiv.org/abs/2009.11644
The cough pre-processing and feature extraction code is available from the following c4science repository: https://c4science.ch/diffusion/10770/
European Commission
10.13039/501100000780
825111
Deep-Learning and HPC to Boost Biomedical Applications for Health
Swiss National Science Foundation
10.13039/501100001711
200020_182009
ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization