EIHW-MTG：第二份DiCOVA挑战系统报告
EIHW-MTG: Second DiCOVA Challenge System Report
摘要：
这项工作提出了一种基于外积的方法来融合从咳嗽，呼吸和语音样本的频谱图中生成的嵌入式表示，用于COVID-19的自动检测。为了从频谱图中提取深度学习的表示，我们比较了从头开始训练的CNN和针对手头任务进行微调的ResNet 18架构的性能。此外，我们还调查了患者的性别和情境注意力机制的使用是否有益。我们的实验使用了作为第二次声学诊断COVID-19（DiCOVA）挑战赛的一部分发布的数据集。结果表明，融合呼吸和语音信息来检测COVID-19是合适的。当使用具有上下文注意力机制的从头开始训练的CNN时，在测试分区上获得84.06%的曲线下面积（AUC）。当使用ResNet 18架构进行特征提取时，基线模型的性能最高，AUC为84.26%。
英文摘要：
This work presents an outer product-based approach to fuse the embedded representations generated from the spectrograms of cough, breath, and speech samples for the automatic detection of COVID-19. To extract deep learnt representations from the spectrograms, we compare the performance of a CNN trained from scratch and a ResNet18 architecture fine-tuned for the task at hand. Furthermore, we investigate whether the patients' sex and the use of contextual attention mechanisms is beneficial. Our experiments use the dataset released as part of the Second Diagnosing COVID-19 using Acoustics (DiCOVA) Challenge. The results suggest the suitability of fusing breath and speech information to detect COVID-19. An Area Under the Curve (AUC) of 84.06% is obtained on the test partition when using a CNN trained from scratch with contextual attention mechanisms. When using the ResNet18 architecture for feature extraction, the baseline model scores the highest performance with an AUC of 84.26%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
