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End-to-End Network Based on Transformer for Automatic Detection of Covid-19
Cong Cai1,2; Bin Liu1; Jianhua Tao1,2,3; Zhengkun Tian1,2; Jiahao Lu4; Kexin Wang1,2
Conference NameInternational Conference on Acoustics, Speech and Signal Processing
Conference Date22-27 May 2022
Conference PlaceSingapore

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.

Sub direction classification智能交互
planning direction of the national heavy laboratory多模态协同认知
Paper associated data
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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