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 language | English |
|---|---|
| Pages | 1-8 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 1 Jul 2022 |
Keywords
- audio pre-processing
- audio noise filtering
- cough segmentation
- COVID-19 open datasets
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