Vocal Biomarker Based COVID-19 Detection Using Convolutional and Deep Neural Networks

The aim of this study is to automate the detection of COVID-19 patients by analysing the acoustic information embedded in cough speech sounds. COVID-19 mainly affects the peoples respiratory system. Then accordingly respiratory-related speech sounds have the potential to hold important information to detect the cause. The principal aim of the Coswara dataset is to analyse the spectrogram representation of cough sound samples and investigate whether COVID-19 patients are improve the frequency content of produced sound signals. Coronavirus effects the whole respiratory system and consequently respiratory-related signals such as cough can possibly contain potential data for the distinctive presence of COVID-19 patients. The Coswara dataset is recorded with labeled categorized into cough sounds with final labeling into Covid-19 positive and Non-Covid classes. Among dataset, we have considered 70% dataset for training and 30% for testing purpose. The Mel Frequency Cepstral Coefficients (MFCCs) are used to extract characteristics of cough sound features from cough speech samples. These features are then used as learning input for machines to label using the self-designed Convolutional Neural Network (CNN) model. After completing the whole process, we got the training AUC (Area Under Curve) for (ROC) 98.84%, validation AUC as 88.23% and testing AUC as 87.09%.