Respiratory sound contains crucial information for the early diagnosis of
fatal lung diseases. Since the COVID-19 pandemic, there has been a growing
interest in contact-free medical care based on electronic stethoscopes. To this
end, cutting-edge deep learning models have been developed to diagnose lung
diseases; however, it is still challenging due to the scarcity of medical data.
In this study, we demonstrate that the pretrained model on large-scale visual
and audio datasets can be generalized to the respiratory sound classification
task. In addition, we introduce a straightforward Patch-Mix augmentation, which
randomly mixes patches between different samples, with Audio Spectrogram
Transformer (AST). We further propose a novel and effective Patch-Mix
Contrastive Learning to distinguish the mixed representations in the latent
space. Our method achieves state-of-the-art performance on the ICBHI dataset,
outperforming the prior leading score by an improvement of 4.08%.

这项研究使用深度学习算法和新型增强技术，通过预处理于视听数据集上的模型实现了高效的呼吸声分类以及肺病诊断，取得了 ICBHI 数据集上 4.08% 的最高成绩。