Cough is a protective reflex conveying information on the state of the respiratory system. Cough assessment has been limited so far to subjective measurement tools or uncomfortable (i.e., non-wearable) cough monitors. This limits the potential of real-time cough monitoring to improve respiratory care. Objective: This paper presents a machine hearing system for audio-based robust cough segmentation that can be easily deployed in mobile scenarios. Methods: Cough detection is performed in two steps. First, a short-term spectral feature set is separately computed in five predefined frequency bands: [0, 0.5), [0.5, 1), [1, 1.5), [1.5, 2), and [2, 5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Second, high-level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 ms long-term frames. Finally, cough detection is carried out using a support vector machine trained with data from different noisy scenarios. The system is evaluated using a patient signal database which emulates three real-life scenarios in terms of noise content. Results: The system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Characteristic (ROC) curve (AUC), outperforming state-of-the-art methods. Conclusion: Our research outcome paves the way to create a device for cough monitoring in real-life situations. Significance: Our proposal is aligned with a more comfortable and less disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns), and national health systems (by reducing hospitalizations).

翻译：咳嗽是一种保护性反射，能传递呼吸系统状态信息。迄今为止，咳嗽评估一直局限于主观测量工具或不舒适（即不可穿戴）的咳嗽监测设备，这限制了实时咳嗽监测改善呼吸护理的潜力。目的：本文提出一种基于音频的鲁棒咳嗽分割机器听觉系统，可轻松部署于移动场景。方法：咳嗽检测分两步进行。首先，在五个预定义频带内分别计算短期频谱特征集：[0, 0.5)、[0.5, 1)、[1, 1.5)、[1.5, 2)及[2, 5.5125] kHz。随后通过特征选择与组合使短期特征集在不同噪声场景下具备足够鲁棒性。其次，通过计算300毫秒长时帧中短期描述符的均值与标准差，实现高层数据表征。最后，使用经不同噪声场景数据训练的支持向量机执行咳嗽检测。该系统采用模拟三种真实噪声场景的患者信号数据库进行评估。结果：系统达到92.71%的灵敏度、88.58%的特异性及90.69%的受试者工作特征曲线下面积，性能优于现有先进方法。结论：本研究为开发适用于真实场景的咳嗽监测设备奠定了基础。意义：本方案符合更舒适、低干扰的患者监测趋势，对患者（允许咳嗽症状的自我监测）、从业者（如治疗评估或咳嗽模式的临床理解）及国家卫生系统（通过减少住院）均具有积极意义。