Neural Cough Counter: A Novel Deep Learning Approach for Cough Detection and Monitoring

Cough is a common symptom associated with respiratory diseases and its analysis plays a crucial role in monitoring the health conditions of affected persons. Traditional cough detection approaches largely fail to identify single cough boundaries when continuous coughs are present, consequently limiting their suitability for effective cough monitoring. In this research, we propose a novel deep learning system for the efficient detection and monitoring of cough events in audio recordings. Our detection pipeline consists of three key steps. First, we perform voice activity detection to eliminate audio silences and focus on relevant segments. Next, we employ a cough classification technique to identify the presence of cough within those audio segments. Finally, we implement cough event detection using a high-performance classification-regression fusion method. Our approach differs from the traditional audio event detection methods in several notable ways: 1) we incorporate a teacher-student framework for the training of our detection model; 2) instead of relying on specific audio features such as MFCC or Mel Spectrogram, our end-to-end system takes the raw audio signal directly as input and outputs the cough boundary timings; 3) the proposed method is general enough to be used for various other sound event monitoring tasks. Our detection model demonstrated strong performance and robustness on both the in-house and public datasets, by achieving cough event detection error-rate scores of 0.31 and 0.32, respectively, which is several times lower than other models. The comparative cough monitoring evaluation of our approach against systems such as the Leicester Cough Monitor and XGBoost demonstrates our method’s superiority by achieving the lowest average hourly symmetric mean absolute error (sMAPE) of 8.48%. The code is available at
https://github.com/FengZongyao/Neural-Cough-Counter
.