A novel cough audio segmentation framework for COVID-19 detection

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.

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Author and article information

Contributors

Alice E. Ashby

Julia A. Meister

Goran Soldar

Khuong An Nguyen

Conference

Publication date (Print): July 2022

Pages: 1-8

Affiliations

[0001]University of Brighton

East Sussex, BN2 4GJ

United Kingdom

Article

DOI: 10.14236/ewic/ODAK22.1

SO-VID: 9f20f36d-61c5-4396-983a-39258bad1e37

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK

Conference acronym: ODAK 2022

Conference number:

Conference location: Brighton, UK

Conference date: June 30-July 1, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Open Data and Knowledge for a Post-Pandemic Era

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/ODAK22.1

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

J HanC BrownJ ChauhanA GrammenosA HasthanasombatD SpathisT XiaP CicutaC Mascolo 2021 Exploring automatic COVID-19 diagnosis via voice and symptoms from crowdsourced data ICASSP 2021 International Conference on Acoustics, Speech and Signal Processing (ICASSP) 8328 8332 IEEE
M PaharM KlopperR WarrenT Niesler 2021 COVID-19 cough classification using machine learning and global smartphone recordings Computers in Biology and Medicine 135 104572
J.F GemmekeD.P EllisD FreedmanA JansenW LawrenceR.C MooreM PlakalM Ritter 2017 Audio set: An ontology and humanlabeled dataset for audio events 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) 776 780 IEEE
M Cohen-McFarlaneR GoubranF Knoefel 2020 Novel coronavirus cough database: Nococoda Ieee Access 8 154087 154094
T XiaD SpathisJ ChA GrammenosJ HanA HasthanasombatE BondarevaT DangA FlotoP CicutaC Mascolo 2021 August COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
P BagadA DalmiaJ DoshiA NagraniP BhamareA MahaleS RaneN AgarwalR Panicker 2020 Cough against covid: Evidence of covid-19 signature in cough sounds arXiv preprint arXiv:2009.08790
D.T PizzoS EstebanM Scetta 2021 IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples arXiv preprint arXiv:2104.13247
N SharmaP KrishnanR KumarS RamojiS.R ChetupalliP.K GhoshS Ganapathy 2020 Coswara–a database of breathing, cough, and voice sounds for COVID-19 diagnosis arXiv preprint arXiv:2005.10548
L OrlandicT TeijeiroD Atienza 2021 The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms Scientific Data 8 1 1 10
G ChaudhariX JiangA FakhryA HanJ XiaoS ShenA Khanzada 2020 Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of COVID-19 from cough arXiv preprint arXiv:2011.13320
J.A MeisterK.A NguyenZ Luo 2021 Audio feature ranking for sound-based COVID-19 patient detection arXiv preprint arXiv:2104.07128
A MurataY TaniguchiY HashimotoY KanekoY TakasakiS Kudoh 1998 Discrimination of productive and non-productive cough by sound analysis Internal Medicine 37 9 732 735
S.H ShinT HashimotoS Hatano 2008 Automatic detection system for cough sounds as a symptom of abnormal health condition IEEE Transactions on Information Technology in Biomedicine 13 4 486 493
Y.A AmrullohU.R AbeyratneV SwarnkarR TriasihA Setyati 2015 Automatic cough segmentation from non-contact sound recordings in pediatric wards Biomedical Signal Processing and Control 21 126 136
C InfanteD ChamberlainR FletcherY ThoratR Kodgule 2017 October Use of cough sounds for diagnosis and screening of pulmonary disease 2017 IEEE Global Humanitarian Technology Conference (GHTC) 1 10 IEEE

Comments

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[1] J HanC BrownJ ChauhanA GrammenosA HasthanasombatD SpathisT XiaP CicutaC Mascolo 2021 Exploring automatic COVID-19 diagnosis via voice and symptoms from crowdsourced data ICASSP 2021 International Conference on Acoustics, Speech and Signal Processing (ICASSP) 8328 8332 IEEE

[2] M PaharM KlopperR WarrenT Niesler 2021 COVID-19 cough classification using machine learning and global smartphone recordings Computers in Biology and Medicine 135 104572

[3] J.F GemmekeD.P EllisD FreedmanA JansenW LawrenceR.C MooreM PlakalM Ritter 2017 Audio set: An ontology and humanlabeled dataset for audio events 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) 776 780 IEEE

[4] M Cohen-McFarlaneR GoubranF Knoefel 2020 Novel coronavirus cough database: Nococoda Ieee Access 8 154087 154094

[5] T XiaD SpathisJ ChA GrammenosJ HanA HasthanasombatE BondarevaT DangA FlotoP CicutaC Mascolo 2021 August COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track

[6] P BagadA DalmiaJ DoshiA NagraniP BhamareA MahaleS RaneN AgarwalR Panicker 2020 Cough against covid: Evidence of covid-19 signature in cough sounds arXiv preprint arXiv:2009.08790

[7] D.T PizzoS EstebanM Scetta 2021 IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples arXiv preprint arXiv:2104.13247

[8] N SharmaP KrishnanR KumarS RamojiS.R ChetupalliP.K GhoshS Ganapathy 2020 Coswara–a database of breathing, cough, and voice sounds for COVID-19 diagnosis arXiv preprint arXiv:2005.10548

[9] L OrlandicT TeijeiroD Atienza 2021 The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms Scientific Data 8 1 1 10

[10] G ChaudhariX JiangA FakhryA HanJ XiaoS ShenA Khanzada 2020 Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of COVID-19 from cough arXiv preprint arXiv:2011.13320

[11] J.A MeisterK.A NguyenZ Luo 2021 Audio feature ranking for sound-based COVID-19 patient detection arXiv preprint arXiv:2104.07128

[12] A MurataY TaniguchiY HashimotoY KanekoY TakasakiS Kudoh 1998 Discrimination of productive and non-productive cough by sound analysis Internal Medicine 37 9 732 735

[13] S.H ShinT HashimotoS Hatano 2008 Automatic detection system for cough sounds as a symptom of abnormal health condition IEEE Transactions on Information Technology in Biomedicine 13 4 486 493

[14] Y.A AmrullohU.R AbeyratneV SwarnkarR TriasihA Setyati 2015 Automatic cough segmentation from non-contact sound recordings in pediatric wards Biomedical Signal Processing and Control 21 126 136

[15] C InfanteD ChamberlainR FletcherY ThoratR Kodgule 2017 October Use of cough sounds for diagnosis and screening of pulmonary disease 2017 IEEE Global Humanitarian Technology Conference (GHTC) 1 10 IEEE

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