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


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
Number of pages8
Publication statusPublished - 1 Jul 2022


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

Cite this