  Deep learning techniques have shown promising results in the automatic
classification of respiratory sounds. However, accurately distinguishing these
sounds in real-world noisy conditions poses challenges for clinical deployment.
Additionally, predicting signals with only background noise could undermine
user trust in the system. In this study, we propose an audio enhancement (AE)
pipeline as a pre-processing step before respiratory sound classification,
aiming to improve performance in noisy environments. Multiple experiments were
conducted using different audio enhancement model structures, demonstrating
improved classification performance compared to the baseline method of noise
injection data augmentation. Specifically, the integration of the AE pipeline
resulted in a 2.59% increase in the ICBHI classification score on the ICBHI
respiratory sound dataset and a 2.51% improvement on our recently collected
Formosa Archive of Breath Sounds (FABS) in multi-class noisy scenarios.
Furthermore, a physician validation study assessed the clinical utility of our
system. Quantitative analysis revealed enhancements in efficiency, diagnostic
confidence, and trust during model-assisted diagnosis with our system compared
to raw noisy recordings. Workflows integrating enhanced audio led to an 11.61%
increase in diagnostic sensitivity and facilitated high-confidence diagnoses.
Our findings demonstrate that incorporating an audio enhancement algorithm
significantly enhances robustness and clinical utility.
