| Autor: |
Mallol Ragolta, Adrià, Cuesta, Helena, Gómez Gutiérrez, Emilia, 1975, Schuller, Björn |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Interspeech 2022. |
| DOI: |
10.21437/interspeech.2022-10291 |
| Popis: |
Comunicació presentada a Interspeech 2022, celebrat del 18 al 22 de setembre de 2022 a Inchon, Corea del Sud. 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 %. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 826506 (sustAGE), and from the Spanish Ministry of Science and Innovation under the Musical AI project (PID2019-111403GB-I00). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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