| Autor: |
XU Chundong, LIU Hao, MIN Yuan, ZHEN Yadi |
| Předmět: |
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| Zdroj: |
Journal of Computer Engineering & Applications; Oct2023, Vol. 59 Issue 19, p99-105, 7p |
| Abstrakt: |
In recent years, sound event localization and detection have been widely used in various fields. The network model of sound event localization and detection based on deep learning is difficult to accurately capture the spatial and channel information of the input feature map, which leads to the difficulty of sound event localization and detection. An attention-based CECANet (coordinate and efficient channel attention network) network model is proposed. Firstly, a coordinate attention module is introduced into the residual module to make the network model pay more attention to the spatial coordinate information of the feature map, and then an efficient channel attention module is added after the average pooling layer to make the network model pay more attention to the channel information between features. The experimental results show that the proposed network model in the TAU-NIGENS Spatial Sound Events 2021 dataset has an overall improvement in performance compared to the baseline model, with F1 and LR improved to 0.720 and 0.728, and ER and LE reduced to 0.393 and 11.71°. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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