Automatically classifying cough sounds is one of the most critical tasks for the diagnosis and treatment of respiratory diseases. However, collecting a huge amount of labeled cough dataset is challenging mainly due to high laborious expenses, data scarcity, and privacy concerns. In this work, our aim is to develop a framework that can effectively perform cough classification even in situations when enormous cough data is not available, while also addressing privacy concerns. Specifically, we formulate a new problem to tackle these challenges and adopt few-shot learning and federated learning to design a novel framework, termed F2LCough, for solving the newly formulated problem. We illustrate the superiority of our method compared with other approaches on COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average F1-Score of 86%. Our results show the feasibility of few-shot learning combined with federated learning to build a classification model of cough sounds. This new methodology is able to classify cough sounds in data-scarce situations and maintain privacy properties. The outcomes of this work can be a fundamental framework for building support systems for the detection and diagnosis of cough-related diseases.

翻译：自动分类咳嗽声音是呼吸系统疾病诊断与治疗中最关键的任务之一。然而，收集大量标注的咳嗽数据集面临显著挑战，主要由于高昂的人工成本、数据稀缺性以及隐私问题。本研究旨在开发一种即使在缺乏海量咳嗽数据的情况下也能有效执行咳嗽分类的框架，同时解决隐私问题。具体而言，我们针对这些挑战提出一个新问题，并采用小样本学习与联邦学习方法设计了一个名为F2LCough的新框架，用于解决这一新定义问题。我们在COVID-19热感面部与咳嗽数据集上展示了该方法相较于其他方法的优越性，其中F2LCough实现了86%的平均F1分数。研究结果表明，将小样本学习与联邦学习相结合构建咳嗽声音分类模型具有可行性。这种新方法能够在数据稀缺场景下完成咳嗽声音分类，同时保持隐私特性。本研究成果可作为构建咳嗽相关疾病检测与诊断支持系统的基础框架。