138
views
0
recommends
+1 Recommend
2 collections
    0
    shares
       
      • Record: found
      • Abstract: found
      • Conference Proceedings: found
      Is Open Access

      A novel cough audio segmentation framework for COVID-19 detection

      proceedings-article
      , , ,
      Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK (ODAK 2022)
      Open Data and Knowledge for a Post-Pandemic Era
      June 30-July 1, 2022
      audio pre-processing, audio noise filtering, cough segmentation, COVID-19 open datasets
      Bookmark

            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.

            Content

            Author and article information

            Contributors
            Conference
            July 2022
            : 1-8
            Affiliations
            [0001]University of Brighton

            East Sussex, BN2 4GJ

            United Kingdom
            Article
            10.14236/ewic/ODAK22.1
            9f20f36d-61c5-4396-983a-39258bad1e37
            © Ashby et al. Published by BCS Learning & Development Ltd. Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK

            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/

            Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK
            ODAK 2022
            Brighton, UK
            June 30-July 1, 2022
            Electronic Workshops in Computing (eWiC)
            Open Data and Knowledge for a Post-Pandemic Era
            Product
            Product Information: 1477-9358BCS 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/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            audio pre-processing,audio noise filtering,cough segmentation,COVID-19 open datasets

            REFERENCES

            1. 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. 2021 COVID-19 cough classification using machine learning and global smartphone recordings Computers in Biology and Medicine 135 104572

            3. 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. 2020 Novel coronavirus cough database: Nococoda Ieee Access 8 154087 154094

            5. 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. 2020 Cough against covid: Evidence of covid-19 signature in cough sounds arXiv preprint arXiv:2009.08790

            7. 2021 IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples arXiv preprint arXiv:2104.13247

            8. 2020 Coswara–a database of breathing, cough, and voice sounds for COVID-19 diagnosis arXiv preprint arXiv:2005.10548

            9. 2021 The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms Scientific Data 8 1 1 10

            10. 2020 Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of COVID-19 from cough arXiv preprint arXiv:2011.13320

            11. 2021 Audio feature ranking for sound-based COVID-19 patient detection arXiv preprint arXiv:2104.07128

            12. 1998 Discrimination of productive and non-productive cough by sound analysis Internal Medicine 37 9 732 735

            13. 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. 2015 Automatic cough segmentation from non-contact sound recordings in pediatric wards Biomedical Signal Processing and Control 21 126 136

            15. 2017 October Use of cough sounds for diagnosis and screening of pulmonary disease 2017 IEEE Global Humanitarian Technology Conference (GHTC) 1 10 IEEE

            Comments

            Comment on this article