  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.
