Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. During the pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as influenza. Among these symptoms, the difference in cough sound played a distinctive role in research. Clinical data collected under the supervision of doctors in a reliable environment were used as the dataset consisting of 16 subjects suspected of COVID-19 with a specific patient demographic. Using the polymerase chain reaction test, the suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patients with non-COVID and with a COVID-19 cough, respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power spectral density based on short-time Fourier transform and mel-frequency cepstral coefficients (MFCC) were chosen as the efficient feature extraction method. From among the classification techniques, the support vector machine (SVM) algorithm was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 was detected with 95.86% classification accuracy thanks to the radial basis function (RBF) kernel function of SVM and the MFCC method. The diagnosis of COVID-19 coughs was performed with 98.6% and 91.7% sensitivity and specificity, respectively.

来自呼吸系统的声音信号在很大程度上被视为人类健康的标志。呼吸道疾病的早期诊断非常重要，因为如果延误，将对人类健康产生不可逆转的影响。深刻震动世界的新冠病毒大流行，更加凸显了这一诊断的重要性。疫情期间，区分症状与流感等类似疾病已成为研究人员关注的焦点。在这些症状中，咳嗽声音的差异在研究中发挥了独特的作用。在可靠环境下在医生监督下收集的临床数据被用作由 16 名疑似患有 COVID-19 的受试者和特定患者群体组成的数据集。利用聚合酶链反应检测，将疑似受试者分为阴性和阳性两组。阴性和阳性标签分别代表非 COVID 和 COVID-19 咳嗽患者。使用信号频谱的 3D 图或瀑布表示，可以揭示咳嗽数据的显着特征。这样，通过应用有效的特征提取和分类技术，就可以将COVID-19与其他咳嗽区分开来。选择基于短时傅立叶变换和梅尔频率倒谱系数（MFCC）的功率谱密度作为有效的特征提取方法。在分类技术中，支持向量机 (SVM) 算法应用于处理后的信号，以识别和分类 COVID-19 咳嗽。在结果评估方面，得益于SVM的径向基函数（RBF）核函数和MFCC方法，对COVID-19受试者的咳嗽进行了检测，分类准确率达到95.86%。COVID-19 咳嗽的诊断灵敏度和特异性分别为 98.6% 和 91.7%。