4048312
doi
10.5281/zenodo.4048312
oai:zenodo.org:4048312
user-deephealth
user-covid-19
user-epfl
user-embeddedsystemslab-epfl
user-eu
Tomas Teijeiro
EPFL
David Atienza
EPFL
The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
Lara Orlandic
EPFL
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
COVID-19
cough sound database
automatic cough classification
<p>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.</p>
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/
Zenodo
2020-09-24
info:eu-repo/semantics/other
4048311
user-deephealth
user-covid-19
user-epfl
user-embeddedsystemslab-epfl
user-eu
1.0
award_title=Deep-Learning and HPC to Boost Biomedical Applications for Health; award_number=825111; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/825111; funder_id=00k4n6c32; funder_name=European Commission;
award_title=ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization; award_number=200020_182009; funder_id=00yjd3n13; funder_name=Swiss National Science Foundation;
1661520890.037499
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md5:5c30a8b00c8bb7783a2c15a48cb8ea9e
https://zenodo.org/records/4048312/files/public_dataset.zip
public
10.5281/zenodo.4048311
isVersionOf
doi