A Cough-based deep learning framework for detecting COVID-19
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In this paper, we propose a deep learning-based framework for detecting COVID-19 positive subjects from their cough sounds. In particular, the proposed framework comprises two main steps. In the first step, we generate a feature representing the cough sound by combining embedding features extracted from a pre-trained model and handcrafted features, referred to as the front-end feature extraction. Then, the combined features are fed into different back-end classification models for detecting COVID-19 positive subjects. The experimental results on the Second 2021 DiCOVA Challenge - Track 2 dataset achieve the top-2 ranking with an AUC score of 81.21 on the blind Test set, improving the challenge baseline by 6.32 and showing competitive with the state-of-the-art systems.
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