Sources of Funding

DeepHealth H2020
SNF ML-edge

The Coughvid project started in April 2020, and its main aim is to study the potential of Artificial Intelligence techniques for identifying patients with COVID-19 from the analysis of coughing sounds recorded directly by the patients and under poorly controlled conditions (typically at home and with a smartphone device). The final objective is to develop a mobile application for large-scale, zero-cost screening of this disease among the global population.

As a first result of the project, the Coughvid Crowdsourcing dataset has been published as open data:

This dataset contains more than 25,000 crowdsourced recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses.

Additionally, more than 2,800 recordings have been manually revised and extensively labeled by expert physicians 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 beyond COVID-19.


Related Publications

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms
Orlandic, Lara; Teijeiro, Tomas; Atienza Alonso, David
2021-06-23Scientific DataPublication funded by DeepHealth H2020 (Deep-Learning and HPC to Boost Biomedical Applications for Health)Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)