Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a significant distribution shift occurs within the test dataset or in a practical scenario, it can substantially decrease the performance. To tackle this issue, we introduce cross-domain adaptation techniques, which transfer the knowledge from a source domain to a distinct target domain. In particular, by considering different stethoscope types as individual domains, we propose a novel stethoscope-guided supervised contrastive learning approach. This method can mitigate any domain-related disparities and thus enables the model to distinguish respiratory sounds of the recording variation of the stethoscope. The experimental results on the ICBHI dataset demonstrate that the proposed methods are effective in reducing the domain dependency and achieving the ICBHI Score of 61.71%, which is a significant improvement of 2.16% over the baseline.

翻译：尽管深度学习技术取得了显著进展，但由于可用数据稀缺，肺音分类仍难以获得令人满意的性能。此外，呼吸音样本收集自多种电子听诊器，这可能会给训练模型引入偏差。当测试数据集或实际场景中出现显著分布偏移时，性能可能大幅下降。为解决这一问题，我们引入了跨域自适应技术，将知识从源域迁移到不同的目标域。具体而言，通过将不同听诊器类型视为独立域，我们提出了一种新颖的听诊器引导的监督对比学习方法。该方法能够缓解与域相关的差异，从而使模型能够区分听诊器记录变化引起的呼吸音差异。在ICBHI数据集上的实验结果表明，所提方法在降低域依赖性方面效果显著，并达到了61.71%的ICBHI分数，较基线提升了2.16%。