dc.identifier.uri |
http://dx.doi.org/10.15488/13001 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/13105 |
|
dc.contributor.author |
Chang, Yi
|
|
dc.contributor.author |
Jing, Xin
|
|
dc.contributor.author |
Ren, Zhao
|
|
dc.contributor.author |
Schuller, Björn W.
|
|
dc.date.accessioned |
2022-11-14T07:13:46Z |
|
dc.date.available |
2022-11-14T07:13:46Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Chang, Y.; Jing, X.; Ren, Z.; Schuller, B.W.: CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds. In: Frontiers in digital health 3 (2022), 799067. DOI: https://doi.org/10.3389/fdgth.2021.799067 |
|
dc.description.abstract |
Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR). Copyright © 2022 Chang, Jing, Ren and Schuller. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Lausanne : Frontiers Media |
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dc.relation.ispartofseries |
Frontiers in digital health 3 (2022) |
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dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
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dc.subject |
cough |
eng |
dc.subject |
COUGHVID |
eng |
dc.subject |
COVID-19 |
eng |
dc.subject |
FluSense |
eng |
dc.subject |
transfer learning |
eng |
dc.subject.ddc |
610 | Medizin, Gesundheit
|
ger |
dc.title |
CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
2673-253X |
|
dc.relation.doi |
https://doi.org/10.3389/fdgth.2021.799067 |
|
dc.bibliographicCitation.volume |
3 |
|
dc.bibliographicCitation.firstPage |
799067 |
|
dc.description.version |
publishedVersion |
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tib.accessRights |
frei zug�nglich |
|