Automated respiratory sound classification supports the diagnosis of pulmonary diseases. However, many deep models still rely on cycle-level analysis and suffer from patient-specific overfitting. We propose PC-MCL (Patient-Consistent Multi-Cycle Learning) to address these limitations by utilizing three key components: multi-cycle concatenation, a 3-label formulation, and a patient-matching auxiliary task. Our work resolves a multi-label distributional bias in respiratory sound classification, a critical issue inherent to applying multi-cycle concatenation with the conventional 2-label formulation (crackle, wheeze). This bias manifests as a systematic loss of normal signal information when normal and abnormal cycles are combined. Our proposed 3-label formulation (normal, crackle, wheeze) corrects this by preserving information from all constituent cycles in mixed samples. Furthermore, the patient-matching auxiliary task acts as a multi-task regularizer, encouraging the model to learn more robust features and improving generalization. On the ICBHI 2017 benchmark, PC-MCL achieves an ICBHI Score of 65.37%, outperforming existing baselines. Ablation studies confirm that all three components are essential, working synergistically to improve the detection of abnormal respiratory events.

翻译：自动化的呼吸音分类有助于肺部疾病的诊断。然而，许多深度模型仍然依赖于周期级别的分析，并且容易受到患者特异性过拟合的影响。我们提出了PC-MCL（患者一致性多周期学习）来解决这些局限性，该方法利用三个关键组件：多周期拼接、三标签公式以及患者匹配辅助任务。我们的工作解决了呼吸音分类中一个多标签分布偏置问题，这是在应用多周期拼接与传统的双标签公式（爆裂音、哮鸣音）时固有的关键问题。这种偏置表现为当正常周期和异常周期结合时，正常信号信息的系统性丢失。我们提出的三标签公式（正常、爆裂音、哮鸣音）通过保留混合样本中所有组成周期的信息来纠正这一问题。此外，患者匹配辅助任务充当了多任务正则化器，鼓励模型学习更鲁棒的特征并提高泛化能力。在ICBHI 2017基准测试中，PC-MCL取得了65.37%的ICBHI分数，优于现有基线。消融研究证实所有三个组件都是必不可少的，它们协同工作以改善异常呼吸事件的检测。