Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification

Respiratory sound classification plays a pivotal role in diagnosing respiratory diseases. While deep learning models have succeeded with various respiratory sound datasets, our experiments indicate that models trained on one dataset often fail to generalize effectively to others, mainly due to data collection and annotation inconsistencies. To address this limitation, we introduce Lungmix, a novel data augmentation technique inspired by Mixup. Lungmix generates augmented data by blending waveforms using loudness while interpolating labels based on their semantic meaning, helping the model learn more generalized representations. Extensive evaluations across three datasets (ICBHI, SPR, and HF) demonstrate that Lungmix significantly enhances model generalization to unseen data. In particular, Lungmix boosts the 4-class classification score by up to 3.55%, attaining performance comparable to models trained on the target dataset directly.