Covid-19 Detection using Spectral and Statistical Features of Cough and Breath Sounds

The pandemic situation due to Corona Virus Disease of 2019 (COVID-19) is significant public health risk around the world. The infected people can spread this virus very quickly. Due to this reason, the early detection is essential to reduce its spread. This research effort aims to develop a method for diagnosis of COVID-19 based on the recording of cough and breath sounds. In this paper, a convolutional neural network (CNN) classifier is applied after train and test splitting for cough and breath sound features. The present work show that the combination of MFCC and cepstrum-based statistical features along with ZCR improve the accuracy of detection to the great extent. It shows great potential in the development of automatic COVID-19 detection tool.