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In IEEE journal of selected topics in signal processing

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.

Ponomarchuk Alexander, Burenko Ilya, Malkin Elian, Nazarov Ivan, Kokh Vladimir, Avetisian Manvel, Zhukov Leonid

2022-Feb

Acoustic signal processing, Big Data applications, biomedical informatics, machine learning, public heathcare, signal detection