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.

翻译：我们提出了一种质量自适应的角度间隔学习框架，通过增强类内紧凑性和类间可分离性来提升特征泛化能力。该框架名为QLung，引入了基于频谱熵和均方根能量的无参考音频质量度量，可根据录音质量自适应地缩放角度间隔。为此，我们提出了一种对数缩放的角度间隔，能在严重类别不平衡情况下稳定训练过程。我们还采用了角度分类器对特征和类别权重进行归一化处理，确保在单位超球面上一致施加间隔惩罚。我们的方法在ICBHI数据集上相比交叉熵基线提升了2.46%的域内分类性能，更重要的是，在SPRSound数据集上取得了超越先前最优方法的域外分类性能。代码已开源：https://github.com/RSC-Toolkit/QLung。