Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67239, first published .
Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation

Jing-Tong Tzeng   1 , BSc ;   Jeng-Lin Li   2 , PhD ;   Huan-Yu Chen   2 , PhD ;   Chu-Hsiang Huang   3 , MD ;   Chi-Hsin Chen   3 , MD ;   Cheng-Yi Fan   3 , MD ;   Edward Pei-Chuan Huang   3 * , MD ;   Chi-Chun Lee   2 * , PhD

1 College of Semiconductor Research, National Tsing Hua University, Hsinchu, Taiwan

2 Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan

3 Department of Emergency Medicine, National Taiwan University Hsin-Chu Hospital, Hsinchu, Taiwan

*these authors contributed equally

Corresponding Author:

  • Chi-Chun Lee, PhD
  • Department of Electrical Engineering
  • National Tsing Hua University
  • 101, Section 2, Kuang-Fu Road
  • Hsinchu, 300
  • Taiwan
  • Phone: 886 35162439
  • Email: cclee@ee.nthu.edu.tw