@misc{10481/80567,
year = {2021},
month = {9},
url = {https://hdl.handle.net/10481/80567},
abstract = {The COVID-19 pandemic has led to the saturation of public health services worldwide. In this scenario, the early diagnosis of SARS-Cov-2 infections can help to stop or slow the spread of the virus and to manage the demand upon health services. This is especially important when resources are also being stretched by heightened demand linked to other seasonal diseases, such as the flu. In this context, the organisers of the DiCOVA 2021 challenge have collected a database with the aim of diagnosing COVID-19 through the use of coughing audio samples. This work presents the details of the automatic system for COVID-19 detection from cough recordings presented by team PANACEA. This team consists of researchers from two European academic institutions and one company: EURECOM (France), University of Granada (Spain), and Biometric Vox S.L. (Spain). We de- veloped several systems based on established signal processing and machine learning methods. Our best system employs a Tea- ger energy operator cepstral coefficients (TECCs) based front- end and Light gradient boosting machine (LightGBM) back- end. The AUC obtained by this system on the test set is 76.31% which corresponds to a 10% improvement over the official base- line.},
organization = {PID2019-104206GB- I00/SRA/10.13039/501100011033},
organization = {PID2019-108040RB- C22/SRA/10.13039/501100011033},
organization = {RESPECT project funded by the French Agence Nationale de la Recherche (ANR)},
organization = {German Research Foundation Deutsche Forschungsgemeinschaft (DFG)},
organization = {Juan de la Cierva- Incorporation Fellowship from the Spanish Ministry of Science, Innovation and Universities (IJCI-2017-32926)},
publisher = {Proceedings of INTERSPEECH 2021},
keywords = {COVID-19},
keywords = {Respiratory sounds},
keywords = {Machine learning},
keywords = {Disease diagnosis},
keywords = {Healthcare},
title = {PANACEA cough sound- based diagnosis of COVID-19 for the DiCOVA 2021 Challenge},
doi = {10.21437/Interspeech.2021-1062},
author = {Kamble, Madhu R. and González López, José Andrés and Grau, Teresa and Espin, Juan M. and Cascioli, Lorenzo and Huang, Yiqing and Gómez Alanís, Alejandro and Patino, José and Font, Roberto and Peinado Herreros, Antonio Miguel and Gómez García, Ángel Manuel and Evans, Nicholas and Zuluaga, Maria A. and Massimiliano, Todisco},
}