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.

通过跨领域适应技术，结合监督对比学习方法，本研究在呼吸音分类领域取得了显著进展，成功减少了不同电子听诊器录制的呼吸音之间的领域依赖并实现了 61.71% 的 ICBHI 得分，相较于基线模型的 2.16% 有了显著提升。