With the periodic rise and fall of COVID-19 and countries being inflicted by
its waves, an efficient, economic, and effortless diagnosis procedure for the
virus has been the utmost need of the hour. COVID-19 positive individuals may
even be asymptomatic making the diagnosis difficult, but amongst the infected
subjects, the asymptomatic ones need not be entirely free of symptoms caused by
the virus. They might not show any observable symptoms like the symptomatic
subjects, but they may differ from uninfected ones in the way they cough. These
differences in the coughing sounds are minute and indiscernible to the human
ear, however, these can be captured using machine learning-based statistical
models. In this paper, we present a deep learning approach to analyze the
acoustic dataset provided in Track 1 of the DiCOVA 2021 Challenge containing
cough sound recordings belonging to both COVID-19 positive and negative
examples. To perform the classification on the sound recordings as belonging to
a COVID-19 positive or negative examples, we propose a ConvNet model. Our model
achieved an AUC score percentage of 72.23 on the blind test set provided by the
same for an unbiased evaluation of the models. The ConvNet model incorporated
with Data Augmentation further increased the AUC-ROC percentage from 72.23 to
87.07. It also outperformed the DiCOVA 2021 Challenge's baseline model by 23%
thus, claiming the top position on the DiCOVA 2021 Challenge leaderboard. This
paper proposes the use of Mel frequency cepstral coefficients as the feature
input for the proposed model.Comment: DiCOVA, top 1st, This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl