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Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough
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 me...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9088778/ https://www.ncbi.nlm.nih.gov/pubmed/35582703 http://dx.doi.org/10.1109/JSTSP.2022.3142514 |
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collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9088778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-90887782022-05-13 Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough IEEE J Sel Top Signal Process Article 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. IEEE 2022-01-13 /pmc/articles/PMC9088778/ /pubmed/35582703 http://dx.doi.org/10.1109/JSTSP.2022.3142514 Text en This article is free to access and download, along with rights for full text and data mining, re-use and analysis. |
spellingShingle | Article Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough |
title | Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough |
title_full | Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough |
title_fullStr | Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough |
title_full_unstemmed | Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough |
title_short | Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough |
title_sort | project achoo: a practical model and application for covid-19 detection from recordings of breath, voice, and cough |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9088778/ https://www.ncbi.nlm.nih.gov/pubmed/35582703 http://dx.doi.org/10.1109/JSTSP.2022.3142514 |
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