  The issue in respiratory sound classification has attained good attention
from the clinical scientists and medical researcher's group in the last year to
diagnosing COVID-19 disease. To date, various models of Artificial Intelligence
(AI) entered into the real-world to detect the COVID-19 disease from
human-generated sounds such as voice/speech, cough, and breath. The
Convolutional Neural Network (CNN) model is implemented for solving a lot of
real-world problems on machines based on Artificial Intelligence (AI). In this
context, one dimension (1D) CNN is suggested and implemented to diagnose
respiratory diseases of COVID-19 from human respiratory sounds such as a voice,
cough, and breath. An augmentation-based mechanism is applied to improve the
preprocessing performance of the COVID-19 sounds dataset and to automate
COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a
DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound
features such as the input function to the 1D CNN instead of adopting the
standard input of MFCC (Mel-frequency cepstral coefficient), and it is
performed better accuracy and performance than previous models.
