# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 2 Related work  
### Cough Data  
There have been several studies which have collected cough data from COVID-19-positive patients. The Cambridge Dataset [12], Coswara [20] and COUGHVID [21] are accessible means of data. For the diagnosis of COVID-19 using cough sound, it is important to collect a large amount of data to increase the diagnostic accuracy. However, data have mainly been collected through crowdsourcing, and through applications, as contacting COVID-19-positive patients is difficult. The cough sound data were recorded using built-in microphones of phones or computers. Precautions were are also required with respect to security, since the collected data included personal data such as the region, gender, and respiratory disease status of the people who provided the data, as well as the cough sound.  
The Cambridge Dataset [12] was created using web applications and Android apps to collect data using crowdsourcing. Users enter their symptoms, record a cough three times, record a breathing sound three times, and enter their COVID-19 test results. Because the data contains user information, a unique ID is created and stored, without collecting personal identifiers or users' e-mail addresses. Healthy data were defined as the data of users who did not have a history of smoking, had not tested positive for COVID-19, and did not have any symptoms. Among the first data shared by the Cambridge researchers, 93 people were healthy and 46 people were COVID-19 positive. The second dataset shared by the Cambridge researchers was divided into train, dev, and test data. The dataset contained data from 725 individuals, 567 of whom were healthy, and 158 who were COVID-19 positive. The length of the recording was at least 2 to 5 s, and the sampling frequency was 16 kHz.  
Figure 3: COVID-19 basic diagnostic flow using the app.  
Coswara [20] was created in India, and collected breathing sounds, cough sounds, and the voices of healthy and unhealthy people using a website application to diagnose COVID-19. For the breathing and cough sounds, shallow sounds and deep sounds were collected, respectively. The pronunciation sounds of three vowels ('eu', 'i', 'u'), and the voices used when counting numbers from 1 to 20 were collected, and the sounds pronounced at normal speed and at high speed were collected. Nine audio files were recorded per participant, and the number of participants in the currently published dataset was 2131 as of 14 September 2021. Among them, 1372 people were healthy, 314 people had respiratory disease, and 99 people were completely recovered. A total of 346 people tested positive for COVID-19, of which 72 people had severe symptoms and 231 people had mild symptoms. In addition, there were 42 asymptomatic patients. The length of the recording was at least 2 to 5 s, and the sampling frequency was 44.1 kHz.  
COUGHVID [21] collected cough sounds from 1 April 2020 to 10 September 2020 using a web application. When the user finished recording the cough sound, the user's age, gender, and current condition were entered. The status of the cough sound data was recorded as COVID-19, symptomatic, or healthy. All data in COUGHVID are in.webm or.ogg format, with a sampling frequency of 48 kHz. The length of the recording was at least 2 to 9 s. There are more than 20,000 pieces of data, and the datasets were filtered using a cough detection algorithm. As there is a risk of collecting the wrong sample using crowdsourced data, the COUGHVID team developed a classifier which analyzes the score of cough sound detection in the data, so that non-cough data can be excluded. The cough sound score is included in the metadata as an item called cough_detected.