| Literature DB >> 35582703 |
Alexander Ponomarchuk1, Ilya Burenko1, Elian Malkin1, Ivan Nazarov1, Vladimir Kokh1, Manvel Avetisian1, Leonid Zhukov1.
Abstract
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.Entities:
Keywords: Acoustic signal processing; Big Data applications; biomedical informatics; machine learning; public heathcare; signal detection
Year: 2022 PMID: 35582703 PMCID: PMC9088778 DOI: 10.1109/JSTSP.2022.3142514
Source DB: PubMed Journal: IEEE J Sel Top Signal Process ISSN: 1932-4553 Impact factor: 7.695