Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation
- DOI
- 10.2196/67239
- Published
- 2025-03-13
- Container
- JMIR AI
- Publisher
- JMIR Publications Inc.
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2196/67239,
title = {Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation},
author = {Jing-Tong Tzeng and Jeng-Lin Li and Huan-Yu Chen and Chun-Hsiang Huang and Chi-Hsin Chen and Cheng-Yi Fan and Edward Pei-Chuan Huang and Chi-Chun Lee},
year = {2025},
journal = {JMIR AI},
doi = {10.2196/67239},
url = {https://doi.org/10.2196/67239}
}RIS
TY - JOUR TI - Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation AU - Jing-Tong Tzeng AU - Jeng-Lin Li AU - Huan-Yu Chen AU - Chun-Hsiang Huang AU - Chi-Hsin Chen AU - Cheng-Yi Fan AU - Edward Pei-Chuan Huang AU - Chi-Chun Lee PY - 2025 JO - JMIR AI DO - 10.2196/67239 UR - https://doi.org/10.2196/67239 ER -
APA
Tzeng, J., Li, J., Chen, H., Huang, C., Chen, C., Fan, C., Huang, E. P., & Lee, C. (2025). Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning–Based Audio Enhancement: Algorithm Development and Validation. JMIR AI. https://doi.org/10.2196/67239
Source records
- crossref · retrieved 2026-09-25T04:22:09.007Z