Abstract
In this paper, we proposed an efficient method for rapid diagnosis of COVID-19 by voice. A novel Strided Convolution Separable Transformer (SC-SepTr) is proposed by modifying the conventional Separable Transformer (SepTr) for audio signal recognition. The proposed method reduces the memory and computational requirements to enable rapid diagnosis of COVID-19. As a result of experiments on Coswara, it was shown that the proposed method perform rapid diagnosis with guaranteeing Area Under the Curve (AUC) performance even for a relatively small amount of learning data.
| Translated title of the contribution | Audio-based COVID-19 diagnosis using separable transformer |
|---|---|
| Original language | Korean |
| Pages (from-to) | 221-225 |
| Number of pages | 5 |
| Journal | Journal of the Acoustical Society of Korea |
| Volume | 42 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2023 |
Keywords
- Breathing
- Cough
- COVID-19
- Separable transformer
- Transformer