Robust detection of audio-cough events using local Hu moments
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13137902
- Type
- D - Journal article
- DOI
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10.1109/JBHI.2018.2800741
- Title of journal
- IEEE Journal of Biomedical and Health Informatics
- Article number
- -
- First page
- 184
- Volume
- 23
- Issue
- 1
- ISSN
- 2168-2194
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Proposed a robust cough detection system to detect coughs from audio signals acquired from a Smartphone in noisy environments (e.g. when carried into a pocket/bag). This system was the basis of the Smartcough App (https://smartcough.wordpress.com/). The app development raised industrial interest for further commercialisation. The 1.5 billion revenue semiconductor company Cirrus Logic funded a validation study to explore commercialisation of the Smartcough app. (£30k) A collaboration with Regional Health System in Castilla León(Spain) has also resulted to deploy the app in a real clinical environment.
Contacts:
Corporate Business Development Manager, Cirrus Logic.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -