CovNet
A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds
- verfasst von
- Yi Chang, Xin Jing, Zhao Ren, Bj枚rn W. Schuller
- 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).
- Organisationseinheit(en)
-
Forschungszentrum L3S
- Externe Organisation(en)
-
Imperial College London
鲍苍颈惫别谤蝉颈迟盲迟 Augsburg
- Typ
- Artikel
- Journal
- Frontiers in digital health
- Band
- 3
- ISSN
- 2673-253X
- Publikationsdatum
- 03.01.2022
- Publikationsstatus
- 痴别谤枚蹿蹿别苍迟濒颈肠丑迟
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Gesundheitsinformatik, Angewandte Informatik, Biomedizintechnik, Medizin (sonstige)
- Elektronische Version(en)
-
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