A novel cough audio segmentation framework for COVID-19 detection

Alice Ashby, Julia A. Meister, Goran Soldar, Khuong An Nguyen

Research output: Contribution to conferencePaperpeer-review

Abstract

Despite its potential, Machine Learning has played little role in the present pandemic, due to the lack of data (i.e., there were not many COVID-19 samples in the early stage). Thus, this paper proposes a novel cough audio segmentation framework that may be applied on top of existing COVID-19 cough datasets to increase the number of samples, as well as filtering out noises and uninformative data. We demonstrate the efficiency of our framework on two popular open datasets.
Original languageEnglish
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 1 Jul 2022
EventSymposium on Open Data and Knowledge for a Post-Pandemic Era - Brighton, United Kingdom
Duration: 30 Jun 20221 Jul 2022
https://universityofbrighton.github.io/odak

Conference

ConferenceSymposium on Open Data and Knowledge for a Post-Pandemic Era
Abbreviated titleODAK 2022
Country/TerritoryUnited Kingdom
CityBrighton
Period30/06/221/07/22
Internet address

Keywords

  • audio pre-processing
  • audio noise filtering
  • cough segmentation
  • COVID-19 open datasets

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