  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. Despite this, recent advancements, such as cough audio recordings,
have emerged as a means to automate the detection of respiratory conditions.
Therefore, this research aims to explore various acoustic features that enhance
the performance of machine learning (ML) models in detecting COVID-19 from
cough signals. It investigates the efficacy of three feature extraction
techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and
Spectral Contrast features, when applied to two machine learning algorithms,
Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and therefore
proposes an efficient CovCepNet detection system. The proposed system provides
a practical solution and demonstrates state-of-the-art classification
performance, with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the
Virufy dataset for COVID-19 detection from cough audio signals.
