Multi-Type Outer Product-Based Fusion of Respiratory Sounds for Detecting COVID-19

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

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

5 Zitate (Scopus)

Abstract

This work presents an outer product-based approach to fuse the embedded representations learnt from the spectrograms of cough, breath, and speech samples for the automatic detection of COVID-19. To extract deep learnt representations from the spectrograms, we compare the performance of specific Convolutional Neural Networks (CNNs) trained from scratch and ResNet18-based CNNs fine-tuned for the task at hand. Furthermore, we investigate whether the patients' sex and the use of contextual attention mechanisms are beneficial. Our experiments use the dataset released as part of the Second Diagnosing COVID-19 using Acoustics (DiCOVA) Challenge. The results suggest the suitability of fusing breath and speech information to detect COVID-19. An Area Under the Curve (AUC) of 84.06 % is obtained on the test partition when using specific CNNs trained from scratch with contextual attention mechanisms. When using ResNet18-based CNNs for feature extraction, the baseline model scores the highest performance with an AUC of 84.26 %.

OriginalspracheEnglisch
Seiten (von - bis)2163-2167
Seitenumfang5
FachzeitschriftProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Jahrgang2022-September
DOIs
PublikationsstatusVeröffentlicht - 2022
Extern publiziertJa
Veranstaltung23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Südkorea
Dauer: 18 Sept. 202222 Sept. 2022

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