Knowledge Commons of Institute of Automation,CAS
End-to-End Network Based on Transformer for Automatic Detection of Covid-19 | |
Cong Cai1,2![]() ![]() ![]() ![]() ![]() ![]() | |
2022 | |
会议名称 | International Conference on Acoustics, Speech and Signal Processing |
会议日期 | 22-27 May 2022 |
会议地点 | Singapore |
摘要 | The novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization. The cumulative number of deaths is more than 4.8 million. Epidemiology experts concur that mass testing is essential for isolating infected individuals, contact tracing, and slowing the progression of the virus. In recent months, some machine learning methods have been proposed utilizing audio cues for COVID-19 detection. However, many works are based on hand-crafted features and deep features to detect COVID-19. There is no evidence that these features are optimal for COVID-19 detection. Therefore, we proposed an end-to-end network based on transformer for automatic detection of COVID-19. It directly learns features from the raw waveform for end-to-end learning, rather than extracting features in advance. We propose a feature extraction module to automatically extract features. And we use the transformer architectures to model the dependencies between the extracted features. It is the first end-to-end learning based on raw waveform for COVID-19 detection. Experiments on COUGHVID dataset show that our method has achieved competitive results. |
七大方向——子方向分类 | 智能交互 |
国重实验室规划方向分类 | 多模态协同认知 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57330 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院人工智能学院 3.中国科学院脑科学与智能技术卓越创新中心 4.天津师范大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Cong Cai,Bin Liu,Jianhua Tao,et al. End-to-End Network Based on Transformer for Automatic Detection of Covid-19[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
End-to-End_Network_B(1210KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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