Name
Abstract | ||
|---|---|---|
Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission. Due to the promising results of deep learning networks in modelling time sequences, we present a temporal-oriented broadcasting residual learning method that achieves efficient computation and high accuracy with a small model size. Based on the EfficientNet architecture, our novel network, named Temporaloriented ResNet (TorNet), constitutes of a broadcasting learning block. The network obtains useful audio-temporal features and higher level embeddings effectively with much less computation than Recurrent Neural Networks (RNNs), typically used to model temporal information. TorNet achieves 72.2% Unweighted Average Recall (UAR) on the INTERPSEECH 2021 Computational Paralinguistics Challenge COVID-19 cough Sub-Challenge, by this showing competitive results with a higher computational efficiency than other state-of-the-art alternatives. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/BHI56158.2022.9926930 | 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) |
Keywords | DocType | ISSN |
SARS-CoV2 detection,deep neural network,efficient neural network,efficient CNN,residual learning | Conference | 2641-3590 |
ISBN | Citations | PageRank |
978-1-6654-8792-4 | 0 | 0.34 |
References | Authors | |
14 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Jing | 1 | 0 | 0.34 |
| Shuo Liu | 2 | 0 | 0.68 |
| Emilia Parada-Cabaleiro | 3 | 4 | 1.77 |
| Andreas Triantafyllopoulos | 4 | 0 | 0.34 |
| Meishu Song | 5 | 5 | 2.13 |
| Zijiang Yang | 6 | 0 | 0.34 |
| Björn Schuller | 7 | 6749 | 463.50 |