# Trunk Borer Identification Based on Convolutional Neural Networks
## 4 Vibration Signal Recognition
### Identification Model  
Generally speaking, audio recognition refers to the process of analyzing, recognizing and classifying sound signals. The audio signal has the characteristics of time series and frequency domain. Convolutional neural network can deal with it properly [29; 31]. A  
Figure 4: Signal characteristic spectrogram.  
convolution neural network can provide translation invariant convolution in time and space. When the idea of convolutional neural network is applied to the modeling of pest vibration signal recognition, the invariance of convolution can be used to overcome the diversity of the vibration signal itself. From the practical point of view, convolutional neural network is also easy to realize for a large-scale parallel operation.  
As mentioned above, this paper designs the convolutional neural network TrunkNet. The network input is an audio signal in wav format, and the main structure is a convolutional layer with five layers in the series. After each layer of convolution, connect the activation function and the mean pooling operation. Finally, the linear classifier is connected to output the recognition result of the network.  
The key role of the first layer is to ensure that the size of the input layer is consistent with the size of the extracted Mel spectrum. The main part of the network is mainly composed of convolutional layers and connection layers. The core idea of convolutional layers is based on the mathematical operation of convolution, using convolutional kernels and parts of the eigengram for convolutional calculations. The same convolutional kernel is continuously convolved with the next region by sliding steps, eventually producing a new feature map and serving as input to the next layer. The general understanding of neural networks is that shallow networks extract edge and local features, and deep networks extract semantic features. After experimental exploration and continuous correction, deep convolutional networks did not improve the effect of the network. The design of the convolution layer is finally determined, the size of the convolution kernel is \(3\times 3\) and the stride is \(1\).  
After the end of the convolution operation, the mean pooling layer is introduced. On the one hand, it reduces the dimension of the feature, and on the other hand, it can effectively retain the information in the feature After the convolution calculation is over, the network reaches 13,376-dimensional features. The fully connected layer integrates the feature information and calculates it according to the recognition probability of the audio. The weights are connected to complete features, and neurons are activated to realize the function of recognition and prediction. In the output layer, the number of neurons is set to 2, and the recognition and prediction results of the two kinds of sound signals are obtained.  
The overall network structure diagram is shown in Figure 5. The ConvBlock, which consists of a convolution part, activation functions and a pooling part, is local feature learning block constructed in this paper.It plays the role of dimensional transformation. The changes in the convolution layer input and output are shown in Table 2. The dimensional change that was previously entered can achieve a good recognition effect. We use averaging for the dimensionality reduction of feature maps. The mean pooling operation is different from the max pooling operation. The operation method of max pooling is to take the maximum value of the feature map corresponding to the convolution kernel as the representative value of this part, and the average value pooling is to take the average value as the representative value. This operation reduces the amount of parameters while maintaining valid features, and preserves more texture features in the map. To prevent overfitting, we set the dropout layer. At the same time, the activation function is introduced to achieve nonlinear changes in the network, which facilitates model convergence.  
Figure 5: Network structure diagram.