标题： CycleGuardian：一种基于改进的深度聚类和对比学习的自动呼吸音分类框架 显示英文标题

标题： CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning

摘要： 听诊在早期呼吸系统和肺部疾病诊断中起着关键作用。 尽管新冠疫情后出现了基于深度学习的自动呼吸音分类方法，但有限的数据集阻碍了性能的提升。 由于正常呼吸成分和噪声成分同时存在于正常和异常呼吸音中，区分正常和异常呼吸音存在挑战。 此外，不同的异常呼吸音表现出相似的异常特征，阻碍了它们的区分。 此外，现有的最先进模型参数量过大，阻碍了在资源受限的移动平台上的部署。 为了解决这些问题，我们设计了一个轻量级网络 CycleGuardian，并提出了一种基于改进的深度聚类和对比学习的框架。 我们首先生成一个混合频谱图以提高特征多样性，并对频谱图进行分组以便于捕捉间歇性异常声音。然后，CycleGuardian 将深度聚类模块与相似性约束聚类组件相结合，以提高捕捉异常特征的能力，并引入带有组混合的对比学习模块以增强异常特征的辨别能力。 多目标优化在训练过程中提升了整体性能。 在实验中，我们使用 ICBHI2017 数据集，遵循官方分割方法且不使用任何预训练权重，我们的方法在 Sp: 82.06$\%$， Se: 44.47$\%$，以及 Score: 63.26$\%$的网络模型大小为 38M，与当前模型相比，我们的方法提高了近 7$\%$，达到了当前最佳性能。 此外，我们将网络部署在安卓设备上，展示了一个全面的智能呼吸音听诊系统。 显示英文摘要

摘要： Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types. Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation. Besides, existing state-of-the-art models suffer from excessive parameter size, impeding deployment on resource-constrained mobile platforms. To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning. We first generate a hybrid spectrogram for feature diversity and grouping spectrograms to facilitating intermittent abnormal sound capture.Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. Multi-objective optimization enhances overall performance during training. In experiments we use the ICBHI2017 dataset, following the official split method and without any pre-trained weights, our method achieves Sp: 82.06 $\%$, Se: 44.47$\%$, and Score: 63.26$\%$ with a network model size of 38M, comparing to the current model, our method leads by nearly 7$\%$, achieving the current best performances. Additionally, we deploy the network on Android devices, showcasing a comprehensive intelligent respiratory sound auscultation system.