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

被引：33

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

来源：

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING | 2022年 / 16卷 / 02期

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

页码：175 / 187

页数：13

共 58 条

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