CASCO: a Contactless Cough Screening System based on Audio Signal Processing

EasyChair Preprint 10651

Abstract

Cough is a common symptom of respiratory disease, which produces a specific sound. Cough detection has great significance to prevent, assess, and control epidemics. This paper proposes CASCO (Cough Analysis System using Short-Time Fourier Transform (STFT) and Convolutional Neural Networks (CNN) in the WeChat mini Program), a cough detection system capable of quantifying the number of coughs through an audio division algorithm. This system combines STFT with CNN, achieving accuracy, precision, recall, and F1-score with 97.0\%, 95.6\%, 98.7\%, and 0.97 respectively in cough detection. The model is embedded into the WeChat mini program to make it feasible to apply cough detection on smartphones and realize large-scale and contactless cough screening. Future research can combine audio and video signals to further improve the accuracy of large-scale cough screening.

Keyphrases: Cough detection, Deep Neural Network, audio signal processing

@booklet{EasyChair:10651,
  author    = {Xinxin Zhang and Hang Liu and Xinru Chen and Rui Qin and Yan Zhu and Wenfang Li and Menghan Hu and Jian Zhang},
  title     = {CASCO: a Contactless Cough Screening System based on Audio Signal Processing},
  howpublished = {EasyChair Preprint 10651},
  year      = {EasyChair, 2023}}