Investigating Feature Selection and Explainability for COVID-19 Diagnostics from Cough Sounds

Flávio Ávila, Amir H. Poorjam, Deepak Mittal, Charles Dognin, Ananya Muguli, Rohit Kumar, Srikanth Raj Chetupalli, Sriram Ganapathy, Maneesh Singh

Abstract: In this paper, we propose an approach to automatically classify COVID-19 and non-COVID-19 cough samples based on the combination of both feature engineering and deep learning models. In the feature engineering approach, we develop a support vector machine classifier over high dimensional (6373D) space of acoustic features. In the deep learning-based approach, on the other hand, we apply a convolutional neural network trained on the log-mel spectrograms. These two methodologically diverse models are then combined by fusing the probability scores of the models. The proposed system, which ranked 9th on the 2021 Diagnosing COVID-19 using Acoustics (DiCOVA) challenge leaderboard, obtained an area under the receiver operating characteristic curve (AUC) of 0.81 on the blind test data set, which is a 10.9% absolute improvement compared to the baseline. Moreover, we analyze the explainability of the deep learning-based model when detecting COVID-19 from cough signals.