This paper aims to automatically detect COVID-19 patients by analysing the acoustic information embedded in coughs. COVID-19 affects the respiratory system, and, consequently, respiratory-related signals have the potential to contain salient information for the task at hand. We focus on analysing the spectrogram representations of coughing samples with the aim to investigate whether COVID-19 alters the frequency content of these signals. Furthermore, this work also assesses the impact of gender in the automatic detection of COVID-19. To extract deep learnt representations of the spectrograms, we compare the performance of a cough-specific, and a Resnet18 pre-trained Convolutional Neural Network (CNN). Additionally, our approach explores the use of contextual attention, so the model can learn to highlight the most relevant deep learnt features extracted by the CNN. We conduct our experiments on the dataset released for the Cough Sound Track of the DiCOVA 2021 Challenge. The best performance on the test set is obtained using the Resnet18 pre-trained CNN with contextual attention, which scored an Area Under the Curve (AUC) of 70.91 at 80% sensitivity.
翻译:本文的目的是通过分析咳嗽中所含的声学信息自动检测COVID-19病人。COVID-19影响呼吸系统,因此,呼吸相关信号有可能包含当前任务所需的显著信息。我们侧重于分析咳嗽样本的光谱显示,以调查COVID-19是否改变这些信号的频率内容。此外,这项工作还评估了性别在自动检测COVID-19方面的影响。为了提取对光谱图的深刻了解,我们比较了特定咳嗽和Resnet18预先培训神经网络的性能。此外,我们的方法探索了对背景的注意,以便模型可以学习突出CNN所提取的最相关的深层了解的特征。我们在DiCOVA 2021挑战的Cough音轨上进行实验。测试集的最佳性能是利用Resnet18预先培训的CNN和背景关注,后者在70.91%至80%敏感度的Curve(AUC)下区域。