Coronavirus disease 2019 (COVID-19) is the currently happening pandemic. Up until mid-2021, the total cases of COVID-19 have reached 171 million worldwide. The virus is mainly transmitted through droplets generated when an infected person coughs, sneezes, or exhales. The most common occurring symptoms are fever, cough, and fatigue. The current diagnosis method is done through Reverse-Transcription Polymer Chain Reaction (RT-PCR) testing. Even though this is the current gold standard, this method has several downsides. The RT-PCR is costly, time-consuming, and can lead to another infection if done improperly. In this paper we try to utilize AI to classify COVID-19 using cough sound. This method can work as a triaging tool to help prioritize a person to get future-diagnosis. In this research, our contribution is trying several feature extractions, imbalance handling and modelling techniques to classify COVID-19 using cough sound. We obtained the best result using the combination of NMF-Spectrogram feature, undersampling method, and SVM. It gives the sensitivity of 90.9%, specificity of 55.6% and overall AUC-ROC of 73.3%. We also discovered that the NMF-Spectrogram feature works better than MFCC-based features. © 2021 IEEE.
COVID-19 Classification Using Cough Sounds