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

aims public health

Automatic COVID-19 disease diagnosis using 1D convolutional neural  network and augmentation with human respiratory sound based on parameters:  cough, breath, and voice