EIHW-MTG: Second DiCOVA Challenge System Report
This work addresses COVID-19 detection for healthcare applications using acoustic data, but it is incremental as it builds on existing methods and datasets.
The paper tackled the problem of automatically detecting COVID-19 by fusing cough, breath, and speech spectrogram representations using an outer product-based approach, achieving an AUC of 84.06% with a CNN and 84.26% with a ResNet18 on the DiCOVA Challenge dataset.
This work presents an outer product-based approach to fuse the embedded representations generated 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 a CNN trained from scratch and a ResNet18 architecture fine-tuned for the task at hand. Furthermore, we investigate whether the patients' sex and the use of contextual attention mechanisms is 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 a CNN trained from scratch with contextual attention mechanisms. When using the ResNet18 architecture for feature extraction, the baseline model scores the highest performance with an AUC of 84.26%.