Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
arXiv:2107.10716 · doi:10.1109/JSTSP.2022.3142514
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.
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels
- Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
- Assessment of Audio Features for Automatic Cough Detection
- An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety