COVID-19 Detection System: A Comparative Analysis of System Performance
  Based on Acoustic Features of Cough Audio Signals
Asmaa Shati Ghulam Mubashar Hassan Amitava Datta
A wide range of respiratory diseases, such as cold and flu, asthma, and
COVID-19, affect people's daily lives worldwide. In medical practice,
respiratory sounds are widely used in medical services to diagnose various
respiratory illnesses and lung disorders. The traditional diagnosis of such
sounds requires specialized knowledge, which can be costly and reliant on human
expertise. Recently, cough audio recordings have been used to automate the
process of detecting respiratory conditions. This research aims to examine
various acoustic features that enhance the performance of machine learning (ML)
models in detecting COVID-19 from cough signals. This study investigates the
efficacy of three feature extraction techniques, including Mel Frequency
Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML
algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and
thus proposes an efficient COVID-19 detection system. The proposed system
produces a practical solution and demonstrates higher state-of-the-art
classification performance on COUGHVID and Virufy datasets for COVID-19
detection.