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

Adria Mallol-Ragolta, Helena Cuesta, Emilia Gómez, Björn W. Schuller

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

9 Zitate (Scopus)

Abstract

The aim of this contribution is to automatically detect COVID- 19 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.

OriginalspracheEnglisch
Titel22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
Herausgeber (Verlag)International Speech Communication Association
Seiten4236-4240
Seitenumfang5
ISBN (elektronisch)9781713836902
DOIs
PublikationsstatusVeröffentlicht - 2021
Extern publiziertJa
Veranstaltung22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Tschechische Republik
Dauer: 30 Aug. 20213 Sept. 2021

Publikationsreihe

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Band6
ISSN (Print)2308-457X
ISSN (elektronisch)1990-9772

Konferenz

Konferenz22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
Land/GebietTschechische Republik
OrtBrno
Zeitraum30/08/213/09/21

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