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.

利用咳嗽声音进行 COVID-19 检测的研究，探讨了增强机器学习模型性能的各种声学特征提取技术，并在两种机器学习算法（支持向量机和多层感知机）上进行了实证研究，提出了一种高效的 COVID-19 检测系统。该系统在 COUGHVID 和 Virufy 数据集上表现出更好的分类性能。