Diagnosis of COVID-19 Using Auditory Acoustic Cues
Rohan Kumar Das, Maulik C. Madhavi, Haizhou Li · 2021
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.