PANACEA Cough Sound-Based Diagnosis of COVID-19 for the DiCOVA 2021 Challenge
| Autor: | Massimiliano Todisco, Alejandro Gomez-Alanis, Yiqing Huang, Maria A. Zuluaga, Angel M. Gomez, Madhu R. Kamble, Juan M. Espin, Jose A. Gonzalez-Lopez, Lorenzo Cascioli, Teresa Grau, Jose Patino, Nicholas Evans, Roberto Font, Antonio M. Peinado |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) medicine.medical_specialty Disease diagnosis Computer science Public health Healthcare COVID-19 Context (language use) Data science Computer Science - Sound Panacea (medicine) Audio and Speech Processing (eess.AS) Respiratory sounds Test set Machine learning Pandemic FOS: Electrical engineering electronic engineering information engineering medicine Gradient boosting Mel-frequency cepstrum Baseline (configuration management) Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | Interspeech 2021. |
| DOI: | 10.21437/interspeech.2021-1062 |
| Popis: | 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. PID2019-104206GB- I00/SRA/10.13039/501100011033 PID2019-108040RB- C22/SRA/10.13039/501100011033 RESPECT project funded by the French Agence Nationale de la Recherche (ANR) German Research Foundation Deutsche Forschungsgemeinschaft (DFG) Juan de la Cierva- Incorporation Fellowship from the Spanish Ministry of Science, Innovation and Universities (IJCI-2017-32926) |
| Databáze: | OpenAIRE |
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