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：呼吸音包含致命肺部疾病早期诊断的关键信息。自新冠疫情以来，基于电子听诊器的非接触式医疗引起了广泛关注。为此，学界已开发出先进的深度学习模型用于肺部疾病诊断，但医疗数据的稀缺性仍使该任务面临挑战。本研究表明，在大规模视觉和音频数据集上预训练的模型可泛化至呼吸音分类任务。此外，我们提出了一种简洁的Patch-Mix增强方法——通过音频频谱图变换器(AST)随机混合不同样本间的补丁。我们进一步提出新颖高效的Patch-Mix对比学习，以在潜空间中区分混合表征。该方法在ICBHI数据集上实现了最优性能，较此前领先结果提升4.08%。