Coronavirus outbreak has affected the entire world adversely this project has been developed in order to help common
masses diagnose their chances of been covid positive just by using coughing sound and basic patient data. Audio classification is
one of the most interesting applications of deep learning. Similar to image data audio data is also stored in form of bits and to
understand and analyze this audio data we have used Mel frequency cepstral coefficients (MFCCs) which makes it possible to
feed the audio to our neural network. In this project we have used Coughvid a crowdsource dataset consisting of 27000 audio
files and metadata of same amount of patients. In this project we have used a 1D Convolutional Neural Network (CNN) to
process the audio and metadata. Future scope for this project will be a model that rates how likely it is that a person is infected
instead of binary classification.
