Piecewise Position Encoding in Convolutional Neural Network for Cough-Based Covid-19 Detection
Jiakun Shen, Xueshuai Zhang, Pengyuan Zhang, Yonghong Yan, Shaoxing Zhang, Zhihua Huang, Yanfen Tang, Yu Wang
Abstract
A fast and efficient COVID-19 detection method is of vital importance to control the spread of the epidemic. Many studies have achieved good performance on cough-based COVID19 detection in the past two years. However, the effect of position information in time-frequency features of cough audio has been less considered in previous studies. Even the convolutional neural networks that are capable to learn position information may be affected by small transformations of input features. Therefore, we propose piecewise position encoding added to time-frequency features to provide supplementary position information explicitly. Considering the differences in recording devices among different people, we use modified instance normalization to achieve better generalization. The proposed methods are validated on three open-sourced datasets and achieve significant improvements in AUC and UAR. The proposed model also shows competitive results in detecting asymptomatic patients.
BibTeX
@inproceedings{icassp2023_piecewisepositio,
title = {Piecewise Position Encoding in Convolutional Neural Network for Cough-Based Covid-19 Detection},
author = {Jiakun Shen and Xueshuai Zhang and Pengyuan Zhang and Yonghong Yan and Shaoxing Zhang and Zhihua Huang and Yanfen Tang and Yu Wang and Fujie Zhang and Aijun Sun},
booktitle = {ICASSP 2023},
year = {2023}
}