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Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
arXiv - CS - Sound Pub Date : 2021-07-12 , DOI: arxiv-2107.10716
Alexander Ponomarchuk, Ilya Burenko, Elian Malkin, Ivan Nazarov, Vladimir Kokh, Manvel Avetisian, Leonid Zhukov

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.

中文翻译:

Project Achoo:从呼吸、声音和咳嗽记录中检测 COVID-19 的实用模型和应用

COVID-19 大流行引起了对感染检测和监控解决方案的极大兴趣和需求。在本文中,我们提出了一种机器学习方法,可以使用消费设备上的录音快速对 COVID-19 进行分类。该方法将信号处理方法与微调的深度学习网络相结合,并提供了用于信号去噪、咳嗽检测和分类的方法。我们还开发并部署了一个移动应用程序,该应用程序使用症状检查器以及语音、呼吸和咳嗽信号来检测 COVID-19 感染。该应用程序在开源数据集和最终用户在 Beta 测试期间收集的嘈杂数据上都显示出强大的性能。
更新日期:2021-07-23
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