|Rohan Kumar Das (NUS, Singapore), Maulik Madhavi (NUS, Singapore), Haizhou Li (NUS, Singapore)|
COVID-19 can be pre-screened based on symptoms and confirmed using other laboratory tests. The cough or speech from patients are also studied in the recent time for detection of COVID-19 as they are indicators of change in anatomy and physiology of the respiratory system. Along this direction, the diagnosis of COVID-19 using acoustics (DiCOVA) challenge aims to promote such research by releasing publicly available cough/speech corpus. We participated in the Track-1 of the challenge, which deals with COVID-19 detection using cough sounds from individuals. In this challenge, we use a few novel auditory acoustic cues based on long-term transform, equivalent rectangular bandwidth spectrum and gammatone filterbank. We evaluate these representations using logistic regression, random forest and multilayer perceptron classifiers for detection of COVID-19. On the blind test set, we obtain an area under the ROC curve (AUC) of 83.49% for the best system submitted to the challenge. It is worth noting that the submitted system ranked among the top few systems on the leaderboard and outperformed the challenge baseline by a large margin.