根据录音质量自适应调整分类边界，提升呼吸音识别准确率。
Quality Adaptive Angular Margin Learning for Respiratory Sound Classification
- 依据频谱熵和能量动态调节角度边距，匹配不同质量录音。
- 在ICBHI数据集上比交叉熵基准提升2.46%准确率。
- 特别适合处理分布外数据，对低质量录音鲁棒性强。
我们提出一种质量自适应的角度边距学习框架QLung，通过增强类内紧凑性和类间可分性来提升特征泛化能力。该框架引入无参考音频质量边距，基于频谱熵和均方根能量计算，根据录音质量自适应调整角度边距。为此，我们设计了对数缩放的角度边距，在严重类别不平衡下保持训练稳定。同时采用归一化特征与类别权重的角分类器，确保边距惩罚在单位超球面上一致施加。实验表明，该方法在ICBHI数据集上的分布内性能相比交叉熵基线提升2.46%，尤其在SPRSound数据集上的分布外性能优于现有最先进方法。代码已公开于https://github.com/RSC-Toolkit/QLung。
原文摘要 · Abstract (English)
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.
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