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The COVID-19 pandemic has led to the saturation of public health servicesworldwide. In this scenario, the early diagnosis of SARS-Cov-2 infections canhelp to stop or slow the spread of the virus and to manage the demand uponhealth services. This is especially important when resources are also beingstretched by heightened demand linked to other seasonal diseases, such as theflu. In this context, the organisers of the DiCOVA 2021 challenge havecollected a database with the aim of diagnosing COVID-19 through the use ofcoughing audio samples. This work presents the details of the automatic systemfor COVID-19 detection from cough recordings presented by team PANACEA. Thisteam consists of researchers from two European academic institutions and onecompany: EURECOM (France), University of Granada (Spain), and Biometric VoxS.L. (Spain). We developed several systems based on established signalprocessing and machine learning methods. Our best system employs a Teagerenergy operator cepstral coefficients (TECCs) based frontend and Light gradientboosting machine (LightGBM) backend. The AUC obtained by this system on thetest set is 76.31% which corresponds to a 10% improvement over the officialbaseline.

PANACEA cough sound-based diagnosis of COVID-19 for the DiCOVA 2021 Challenge