The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions. In this paper we propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection. The application showed robust performance on both open sourced datasets and on the noisy data collected during beta testing by the end users.
翻译:COVID-19大流行造成了对感染检测和监测解决方案的极大兴趣和需求。在本文中,我们建议采用机械学习方法,利用消费设备上的记录对COVID-19进行快速分解;这种方法将信号处理方法与经过精细调整的深层学习网络结合起来,并提供信号去注、咳嗽检测和分类方法;我们还开发并部署了移动应用程序,利用症状检查器以及语音、呼吸和咳嗽信号来检测COVID-19感染;该应用程序在开放源数据集和终端用户进行贝塔测试期间收集的噪音数据上都表现良好。