A Bird Voice Recognition Model Based on Attention Mechanism in Shennongjia
[Objective]A bird voice recognition model based on attention mechanism is proposed to address the issues of incomplete feature extraction in traditional bird voice recognition methods,in order to improve the accuracy of bird voice recognition and improve the bird monitoring system.[Method]We optimized the convolution kernel of the ResNet34 model,introduced the attention mechanism—CBAM module,and optimized the structure of the CBAM module to construct a CRNet recognition model.We used the xeno-canto network database to construct a dataset of rare birds in Shennongjia to validate and analyze the model,in order to achieve sound recognition of birds in the Shennongjia region.[Result]1)Regarding model accuracy,the proposed bird sound recognition model achieves an accuracy of 89％for bird sound recognition,which is 2％and 9％higher than the CBAM-ResNet and ResNet34 models,respectively.The model has good prediction performance in most categories,with an accuracy of over 85％.2)Regarding model parameters,CRNet has a parameter count of 5.36 M and a computational load of 73.33 M,which is about 75％lower than ResNet34 in terms of parameter count,while the computational load is about 3％lower than that of ResNet34.3)Compared with other deep learning models,CRNet outperforms MelResNet and VGG11 regarding F1 score,mAP,and accuracy.[Conclusion]The proposed Shennongjia bird sound recognition model has a high recognition accuracy,providing technical support for the protection and monitoring of birds in the Shennongjia area.
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