# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 1 Introduction  
COVID-19 is an acute respiratory infection that develops from SARS-CoV-2, a new type of coronavirus that was first reported in November 2019. This is a pandemic that continues worldwide as of November 2022, with a cumulative number of confirmed cases of 640 million and fatalities of 6.6 million. A characteristic of coronavirus is that it spreads swiftly and readily. Consequently, studies are being actively conducted on how to analyze how the coronavirus spreads and how to prevent its spread [1; 2; 3]. The omicron mutation, which has a low fatality rate but a very high transmission rate, has become the dominant variant. As the number of confirmed cases increases quickly, so do the numbers of severely sick patients and fatalities. Additionally, even though the fatality rate is low, an infection of COVID-19 may still be fatal for the elderly or those with underlying illnesses; thus, it is crucial to stop the spread of the disease by obtaining early diagnosis and treatment. The main route of infection is known to be droplets and respiratory secretions in the air produced by infected individuals. The most frequently used diagnostic test for COVID-19 is real-time reverse transcription polymerase chain reaction (real-time RT-PCR), which is a technique for amplifying and identifying a particular coronavirus gene [4]. Because it has the greatest sensitivity and specificity and can detect even minute amounts of virus in a sample, this test method is used as a worldwide standard. Its drawbacks include the need for specialized tools, reagents, and skilled professionals, as well as the comparatively lengthy turnaround time of roughly 24 h for diagnostic outcomes. Worldwide studies are being performed to find ways other than genetic testing to identify those who are COVID-19 positive. Chest X-ray or chest computed tomography (CT) images have been offered as the input for deep learning models [5; 6]. In a study that used the fact that COVID-19 positive individuals have a specific volatile substance thatis distinct from that of non-infected individuals, a COVID-19 detection scheme using the olfactory abilities of dogs was proposed [7]. Another study proposed using heart rate, sleep time, and activity data collected using wearable sensors to detect COVID-19 [8]. In a study that examined correlations with positive COVID-19, from findings using 42 characteristics that included fever, cough, chest CT, and body temperature, the characteristic that showed the strongest positive correlation was cough [9]. Based on this, this study investigates a method for identifying COVID-19-infected people using cough. Many studies are being conducted to identify COVID-19 through cough sounds in order to allow low-cost and quick large-scale diagnostic testing [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Respiratory symptoms are one of the features of COVID-19 infection; hence, the data provided by the sound of coughing is used. A sound contains many features [24], and so does the sound of a cough. The deep learning model trained using these features can determine whether a cough sound is from a COVID-19-infected individual. Looking at previous studies that used cough sounds, the amount of data is not large. [PERSON] et al. [20] used only the Cambridge dataset [20], and [PERSON] et al. [21] used Coswara [25] and Virufy [26] as datasets to study a COVID-19 diagnostic model. [PERSON] et al. [22] used only the COUGHVID dataset [27]. In order to improve the stability and accuracy of the results, the quantity and quality of data are very important. Therefore, in this paper, all of the Cambridge, Coswara, and COUGHVID datasets were used, and a high-quality dataset was built through preprocessing. In addition, when selecting a feature set in previous studies, the model was trained by combining several spectral-based features simply because it was a feature mainly used in speech. In this study, we propose a feature set optimized for COVID-19 diagnosis.