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Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough (2107.10716v2)
Published 12 Jul 2021 in eess.SP, cs.LG, cs.SD, and eess.AS
Abstract: 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.
- Alexander Ponomarchuk (3 papers)
- Ilya Burenko (4 papers)
- Elian Malkin (2 papers)
- Ivan Nazarov (17 papers)
- Vladimir Kokh (11 papers)
- Manvel Avetisian (7 papers)
- Leonid Zhukov (11 papers)