Quality Adaptive Angular Margin Learning for Respiratory Sound Classification
For researchers in audio-based medical diagnosis, this work provides a method to handle variable recording quality and class imbalance, though the gains are incremental.
QLung improves respiratory sound classification by using a quality-adaptive angular margin, achieving 2.46% improvement on ICBHI and state-of-the-art out-of-distribution performance on SPRSound.
We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability. Our framework, titled QLung, introduces a no-reference audio quality margin derived from spectral entropy and root-mean-square energy, which adaptively scales angular margins based on recording quality. To this end, we propose a log-scaled angular margin that stabilizes training under severe class imbalance. We also use an angular classifier that normalizes features and class weights, ensuring margin penalties are applied consistently on the unit hypersphere. Our approach improves in-distribution performance on the ICBHI dataset by 2.46\% over the cross-entropy baseline, and most significantly, achieves the strongest out-of-distribution performance on the SPRSound dataset compared to prior state-of-the-art methods. Code is available at https://github.com/RSC-Toolkit/QLung.