A novel cough audio segmentation framework for COVID-19 detection

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

doi:10.14236/ewic/ODAK22.1

A novel cough audio segmentation framework for COVID-19 detection

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

School of Arch, Tech and Eng

Research output: Contribution to conference › Paper › peer-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 language	English
Pages	1-8
Number of pages	8
DOIs	https://doi.org/10.14236/ewic/ODAK22.1
Publication status	Published - 1 Jul 2022
Event	Symposium on Open Data and Knowledge for a Post-Pandemic Era - Brighton, United Kingdom Duration: 30 Jun 2022 → 1 Jul 2022 https://universityofbrighton.github.io/odak

Conference

Conference	Symposium on Open Data and Knowledge for a Post-Pandemic Era
Abbreviated title	ODAK 2022
Country/Territory	United Kingdom
City	Brighton
Period	30/06/22 → 1/07/22
Internet address	https://universityofbrighton.github.io/odak

Keywords

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

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10.14236/ewic/ODAK22.1Licence: Unspecified

Cite this

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title = "A novel cough audio segmentation framework for COVID-19 detection",

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.",

keywords = "audio pre-processing, audio noise filtering, cough segmentation, COVID-19 open datasets",

author = "Alice Ashby and Meister, \{Julia A.\} and Goran Soldar and Nguyen, \{Khuong An\}",

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AU - Nguyen, Khuong An

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Y1 - 2022/7/1

N2 - 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.

AB - 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.

KW - audio pre-processing

KW - audio noise filtering

KW - cough segmentation

KW - COVID-19 open datasets

U2 - 10.14236/ewic/ODAK22.1

DO - 10.14236/ewic/ODAK22.1

M3 - Paper

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T2 - Symposium on Open Data and Knowledge for a Post-Pandemic Era

Y2 - 30 June 2022 through 1 July 2022

ER -

A novel cough audio segmentation framework for COVID-19 detection

Abstract

Conference

Keywords

Access to Document

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Cite this