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-3 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.
翻译:在本文中,我们提出了一个深层次的学习基础框架,用于检测从咳嗽声音中检测COVID-19阳性主题,特别是拟议框架包括两个主要步骤。第一步,我们通过结合从预先训练的模型和手工艺特征中提取的嵌入特征(称为前端特征提取),生成一个代表咳嗽声音的特征。然后,这些综合特征被注入不同的后端分类模型中,用于检测COVID-19阳性主题。第二个2021年DiCOVA挑战-第二轨道数据集的实验结果达到了最高三级,在盲人测试集中,ACU得分为81.21,将挑战基线提高6.32,并显示与最先进的系统具有竞争力。