Spatial Attention Deep Convolution Neural Network for Call Recognition of Marine Mammal

被引:2
作者
Yang, Honghui [1 ]
Huang, Yining [1 ]
Liu, Yuqi [1 ]
机构
[1] Northwestern Polytech Univ, Xian 710000, Peoples R China
来源
PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022 | 2023年 / 1010卷
基金
中国国家自然科学基金;
关键词
Spatial attention; Deep convolution neural network; Call recognition of marine mammal marine;
D O I
10.1007/978-981-99-0479-2_251
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the task of call recognition of marine mammal, the time-frequency effective feature spatial focus method is proposed. The method tends to use Spatial Attention (SA) to help Deep Convolution Neural Network (DCNN) reach better recognition performance. The Time-Frequency Image Recognition_DCNN (TFIR_DCNN) of the method is designed first. The proposed network realizes the recognition task with time-frequency images of the calls as input. Features of time-frequency image contain the noise features and the call features. Then, to help TFIR_DCNN focus on the spatial position of the call features in time and frequency domain, the SA is added to TFIR_DCNN. SA can generate and multiply weight tensor to the feature maps so the features of the call are emphasized on spatial to help TFIR_DCNN recognizing. The paper designs a call recognition of marine mammal experiment. In the experiment, model from the proposed method achieves 66.14% in recall, 73.55% in precision, 66.14% in accuracy, 69.95% in F1-score and gets 5.29% in recall, 10.01% in precision, 5.29% in accuracy, 7.48% in F1-score higher than the control model.
引用
收藏
页码:2725 / 2733
页数:9
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