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

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 %. Copyright © 2022 ISCA.

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

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. Copyright © 2021 ISCA.