GaP-Aug: Gamma Patch-Wise Correction Augmentation Method for Respiratory Sound Classification

Abstract

Automated auscultation analysis using electronic stethoscope has received growing interest in clinical applications. Recently, researchers showed successes by using deep learning methods to distinguish between pathological respiratory sound classes. Nevertheless, the challenge persists due to the scarcity of abnormal samples, and the distinct characteristics between low-pitched and discontinuous crackles and high-pitched and continuous wheezes. In this study, we proposed a novel augmentation method, namely gamma patch-wise correction augmentation, which directly operates on spectrograms to handle with these two challenges. We achieved state-of-the-art performances on both 60-40 official split and 80-20 cross-validation of the public ICBHI dataset, outperforming previous top-performing studies by 11.82% in sensitivity and 5.27% in ICBHI score. Furthermore, Grad-CAM analysis shows that our approach better preserves the distinctive characteristics of crackles and wheezes than SpecAug.