标题： 从无标签数据中使用不同预训练领域的对比学习发现 COVID-19 咳嗽和呼吸模式 显示英文标题

标题： Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains

摘要： 快速发现新疾病，如 COVID-19，可以实现及时的流行病响应，防止大规模传播并保护公共健康。 然而，对此问题的研究努力有限。 在本文中，我们提出了一种基于对比学习的建模方法，用于从非 COVID 咳嗽中发现 COVID-19 的咳嗽和呼吸模式。 为了验证我们的模型，使用四个大型音频数据集和一个图像数据集进行了广泛的实验。 我们进一步探讨了不同因素的影响，例如领域相关性和增强顺序对预训练模型的影响。 我们的结果表明，所提出的模型能够有效地从未标记的数据和标记的非 COVID 咳嗽中区分 COVID-19 咳嗽和呼吸，准确率分别高达 0.81 和 0.86。 这项工作的发现将指导未来的研究，以早期检测新疾病的爆发。 显示英文摘要

摘要： Rapid discovery of new diseases, such as COVID-19 can enable a timely epidemic response, preventing the large-scale spread and protecting public health. However, limited research efforts have been taken on this problem. In this paper, we propose a contrastive learning-based modeling approach for COVID-19 coughing and breathing pattern discovery from non-COVID coughs. To validate our models, extensive experiments have been conducted using four large audio datasets and one image dataset. We further explore the effects of different factors, such as domain relevance and augmentation order on the pre-trained models. Our results show that the proposed model can effectively distinguish COVID-19 coughing and breathing from unlabeled data and labeled non-COVID coughs with an accuracy of up to 0.81 and 0.86, respectively. Findings from this work will guide future research to detect an outbreak of a new disease early.