论文标题
跨数据库互联-19转移学习，咳嗽检测，咳嗽分割和数据增强
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation
论文作者
论文摘要
本文解决了基于咳嗽的Covid-19检测问题。我们提出了一种跨数据库转移学习方法，以通过纳入咳嗽检测，咳嗽分割和数据增强来提高COVID-19检测的性能。第一个旨在删除概率较低的非咳嗽信号和咳嗽信号。第二个针对将波形中的几个咳嗽分离为单个咳嗽。第三个旨在增加深度学习模型的样本数量。这三个处理块很重要，因为我们的发现揭示了相对于没有这些块的基线方法的大幅度改进。进行了一项消融研究以优化超参数，发现α混合是通过这种增强方法改善模型性能的其他重要因素。对这项研究的摘要，先前对同一评估集进行了研究，以洞悉基于咳嗽的COVID-19检测方法的不同方法。
This paper addresses issues on cough-based COVID-19 detection. We propose a cross-dataset transfer learning approach to improve the performance of COVID-19 detection by incorporating cough detection, cough segmentation, and data augmentation. The first aimed at removing non-cough signals and cough signals with low probability. The second aimed at segregating several coughs in a waveform into individual coughs. The third aimed at increasing the number of samples for the deep learning model. These three processing blocks are important as our finding revealed a large margin of improvement relative to the baseline methods without these blocks. An ablation study is conducted to optimize hyperparameters and it was found that alpha mixup is an important factor among others in improving the model performance via this augmentation method. A summary of this study with previous studies on the same evaluation set was given to gain insights into different methods of cough-based COVID-19 detection.
