# Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
## 1 Introduction  
Life and health have always been an important topic of global human concern. In order to ensure the safety of human life, health, and safety, different countries have established their own healthcare systems. Studies have shown that there is an upward trend in government and individual health expenditures in some regions [1, 2]. However, some countries still need to improve their healthcare systems [3, 4]. In recent years, the emergence of the COVID-19 epidemic has greatly tested the sustainability of health systems in many countries [5], and the lives of people in some developing countries have also been greatly impacted [6]. In order to better deal with the national medical financial risks brought by the COVID-19 epidemic, countries and the research community are actively taking measures to deal with the epidemic [7], especially in the detection of the COVID-19 disease [8, 9]. At present, the main detection method for COVID-19 is to use throat swabs and nasal swabs to collect the throat parts (i.e., the posterior pharyngeal wall) of the subjects [10]. Although it is a standard method, there are many potential risks in its sampling and detection process. Before sampling, the tested people need to wait in line for nucleic acid detection. During sampling, the medical staff will also have close contact with the subjects during on-site collection. Such a sampling process violates social distancing and greatly increases the risk of cross-infection. After sampling, the medical staff will send the collected samples back to the hospital and test them with nucleic acid testing reagents. The tested person will need to wait about four to five hours to obtain the test result. This detection process is not efficient and increases the chances of virus transmission. According to medical analysis [11], the new coronavirus is an RNA virus, and its surface protein will first attack human respiratory epithelial cells, causing severe inflammation of the respiratory system. It leads to the difference in acoustic characteristics between patients with COVID-19 and healthy people [12; 13; 14; 15]. Many research results show that, in addition to visual information, audio signals produced by the human body can also provide reliable basis for medical diagnosis [16; 17; 18]. Meanwhile, with the development of artificial intelligence, it has become a reality for wearable devices to collect and analyze audio signals [19]. During the COVID-19 epidemic, many universities tried to collect the cough sounds of COVID-19 patients and healthy people, and use machine learning to classify two cough sounds. In September 2020, the team at Cambridge University extracted several features of cough sounds through manual extraction and transfer learning, and divided them into three tasks for research. The first is to distinguish COVID-positive from non-COVID samples, the second is to distinguish COVID-positive with a cough from non-COVID with a cough, and the third is to distinguish COVID-positive coughs from non-COVID asthma coughs [20]. The research results showed that the performance of all tasks remained above 0.8 in the area under the curve (AUC). In the same year, the Massachusetts Institute of Technology also made relevant reports on the research based on cough sound detection [21]. The team widely collected audio signals on its own website, extracted the acoustic characteristics of cough sounds, and then used CNN to classify cough sounds. The team verified the specificity value of 94.2% and AUC of 0.97 on the dataset. In January 2021, [PERSON] et al. [22] proposed a cough analysis system that could classify audio feature tensors and detect the severity of COVID-19 infectors. In the AICovidVN 115M COVID-19 challenge, [PERSON]'s team [23] extracted multiple features based on the dataset provided by the competition, combined them, and implemented different normalization processing methods for the input data.