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.

翻译：各类呼吸系统疾病，如感冒、流感、哮喘和COVID-19，对全球人们的日常生活产生影响。在医疗实践中，呼吸音被广泛应用于诊断各种呼吸系统疾病和肺部功能紊乱。传统上，对这类声音的诊断需要专业知识，成本高昂且依赖人类专家的判断。近年来，咳嗽录音已被用于自动化检测呼吸系统状况。本研究旨在探讨多种声学特征如何提升机器学习（ML）模型从咳嗽信号中检测COVID-19的性能。研究考察了三种特征提取技术——包括梅尔频率倒谱系数（MFCC）、色度特征和谱对比特征——在两种机器学习算法（支持向量机（SVM）和多层感知器（MLP））上的有效性，并据此提出了一种高效的COVID-19检测系统。所提出的系统提供了一种实用解决方案，在COUGHVID和Virufy数据集上展现出更优的COVID-19检测分类性能，达到当前最佳水平。