Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection

Abstract

As the COVID-19 pandemic continues, rapid non-invasive testing has become essential. Recent studies and benchmarks motivates the use of modern artificial intelligence (AI) tools that utilize audio waveform spectral features of coughing for COVID-19 diagnosis. In this paper, we describe the system we developed for COVID-19 cough detection. We utilize features directly extracted from the coughing audio and use deep learning algorithms to develop automated diagnostic tools for COVID-19. In particular, we develop a unique modification of the VGG13 deep learning architecture for audio analysis that uses log-mel spectrograms and a combination of binary cross entropy and focal losses. This unique modification enabled the model to achieve highly robust classification of the DiCOVA 2021 COVID-19 data. We also explore the use of data augmentation and an ensembling strategy to further improve the performance on the validation and the blind test datasets. Our model achieved an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%.