Ngo, Dat and Pham, Lam and Hoang, Truong and Kolozali, Sefki and Jarchi, Delaram (2022) Audio-Based Deep Learning Frameworks for Detecting COVID-19. In: 2022 30th European Signal Processing Conference (EUSIPCO), 2022-08-29 - 2022-09-02, Belgrade, Serbia.
Ngo, Dat and Pham, Lam and Hoang, Truong and Kolozali, Sefki and Jarchi, Delaram (2022) Audio-Based Deep Learning Frameworks for Detecting COVID-19. In: 2022 30th European Signal Processing Conference (EUSIPCO), 2022-08-29 - 2022-09-02, Belgrade, Serbia.
Ngo, Dat and Pham, Lam and Hoang, Truong and Kolozali, Sefki and Jarchi, Delaram (2022) Audio-Based Deep Learning Frameworks for Detecting COVID-19. In: 2022 30th European Signal Processing Conference (EUSIPCO), 2022-08-29 - 2022-09-02, Belgrade, Serbia.
Abstract
This paper evaluates a wide range of audio-based deep learning frameworks applied to the breathing, cough, and speech sounds for detecting COVID-19. In general, the audio recording inputs are transformed into low-level spectrogram features, then they are fed into pre-trained deep learning models to extract high-level embedding features. Next, the dimension of these high-level embedding features are reduced before fine-tuning using Light Gradient Boosting Machine (LightGBM) as a back-end classification. Our experiments on the Second DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score, sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%, respectively. Based on these scores, our method outperforms the state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00% and 8.33% in terms of AUC, F1 score and sensitivity score, respectively.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | low-level spectrogram feature; high-level embedding feature; pre-trained model; convolutional neural network |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 16 Nov 2023 17:43 |
Last Modified: | 07 Aug 2024 20:13 |
URI: | http://repository.essex.ac.uk/id/eprint/33982 |
Available files
Filename: EUSIPCO22_Covid_19.pdf