Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough

被引:30
|
作者
Ponomarchuk, Alexander [1 ]
Burenko, Ilya [1 ]
Malkin, Elian [1 ]
Nazarov, Ivan [1 ]
Kokh, Vladimir [1 ]
Avetisian, Manvel [1 ]
Zhukov, Leonid [1 ]
机构
[1] Sberbank Russia, AI Lab, Moscow 228827, Russia
关键词
COVID-19; Feature extraction; Acoustics; Mel frequency cepstral coefficient; Hidden Markov models; Pandemics; Task analysis; Acoustic signal processing; signal detection; biomedical informatics; public heathcare; machine learning; Big Data applications; AUDIO; SOUND;
D O I
10.1109/JSTSP.2022.3142514
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The COVID-19 pandemic created significant interest and demand for infection detection and monitoring solutions. In this paper, we propose a machine learning method to quickly detect COVID-19 using audio recordings made on consumer devices. The approach combines signal processing and noise removal methods with an ensemble of fine-tuned deep learning networks and enables COVID detection on coughs. We have also developed and deployed a mobile application that uses a symptoms checker together with voice, breath, and cough signals to detect COVID-19 infection. The application showed robust performance on both openly sourced datasets and the noisy data collected during beta testing by the end users.
引用
收藏
页码:175 / 187
页数:13
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