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Cough-based COVID-19 detection with contextual attention convolutional neural networks and gender information

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dc.contributor.author Mallol Ragolta, Adrià
dc.contributor.author Cuesta, Helena
dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
dc.contributor.author Schuller, Björn
dc.date.accessioned 2023-03-01T13:48:50Z
dc.date.available 2023-03-01T13:48:50Z
dc.date.issued 2021
dc.identifier.citation Mallol-Ragolta A, Cuesta H, Gómez E, Schuller BW. Cough-based COVID-19 detection with contextual attention convolutional neural networks and gender information. In: Proc. Interspeech 2021; 2021 Aug 30-Sep 3; Brno, Czech Republic. [Baixas]: International Speech Communication Association; 2021. p. 941-5. DOI: 10.21437/Interspeech.2021-1052
dc.identifier.uri http://hdl.handle.net/10230/55988
dc.description Comunicació presentada a INTERSPEECH 2021, celebrat del 30 d'agost al 3 de setembre de 2021 a Brno, Txèquia.
dc.description.abstract The aim of this contribution is to automatically detect COVID19 patients by analysing the acoustic information embedded in coughs. COVID-19 affects the respiratory system, and, consequently, respiratory-related signals have the potential to contain salient information for the task at hand. We focus on analysing the spectrogram representations of cough samples with the aim to investigate whether COVID-19 alters the frequency content of these signals. Furthermore, this work also assesses the impact of gender in the automatic detection of COVID-19. To extract deep-learnt representations of the spectrograms, we compare the performance of a cough-specific, and a Resnet18 pre-trained Convolutional Neural Network (CNN). Additionally, our approach explores the use of contextual attention, so the model can learn to highlight the most relevant deep-learnt features extracted by the CNN. We conduct our experiments on the dataset released for the Cough Sound Track of the DICOVA 2021 Challenge. The best performance on the test set is obtained using the Resnet18 pre-trained CNN with contextual attention, which scored an Area Under the Curve (AUC) of 70.91 % at 80 % sensitivity.
dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 826506 (sustAGE) and No. 770376 (TROMPA). Further funding has been received from the FI Predoctoral Grant 2018FI-B01015 from AGAUR, Generalitat de Catalunya.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher International Speech Communication Association (ISCA)
dc.relation.ispartof Proc. Interspeech 2021; 2021 Aug 30-Sep 3; Brno, Czech Republic. [Baixas]: International Speech Communication Association; 2021. p. 941-5.
dc.rights © 2021 ISCA
dc.title Cough-based COVID-19 detection with contextual attention convolutional neural networks and gender information
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.21437/Interspeech.2021-1052
dc.subject.keyword COVID-19
dc.subject.keyword acoustics
dc.subject.keyword machine learning
dc.subject.keyword respiratory diagnosis
dc.subject.keyword healthcare
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826506
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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