Quality Adaptive Angular Margin Learning for Respiratory Sound Classification
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 da...
| Authors | Yoon Tae Kim, Heejoon Koo, Miika Toikkanen et al. |
|---|---|
| Primary category | cs.SD |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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