Audio-Based Deep Learning Frameworks for Detecting COVID-19
30th European Signal Processing Conference, EUSIPCO 2022
; 2022-August:1233-1237, 2022.
Article
in English
| Scopus | ID: covidwho-2102361
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. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
30th European Signal Processing Conference, EUSIPCO 2022
Year:
2022
Document Type:
Article
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