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%.

翻译：呼吸音包含对于致命性肺部疾病早期诊断至关重要的信息。自新冠疫情暴发以来，基于电子听诊器的非接触式医疗日益受到关注。为此，研究者开发了尖端深度学习模型用于肺部疾病诊断，但医疗数据稀缺仍是一大挑战。本研究表明，在大型视觉和音频数据集上预训练的模型可迁移至呼吸音分类任务。此外，我们引入了一种简洁的Patch-Mix数据增强方法，该方法通过音频频谱图变换器（AST）随机混合不同样本的局部片段，并进一步提出新颖高效的Patch-Mix对比学习，用于在潜在空间中区分混合表征。我们的方法在ICBHI数据集上实现了最优性能，较此前领先结果提升4.08%。