## 2 Materials and Methods
### The Proposed Model
#### 2.2.3. Deep Learning Framework
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Operator** & **Resolution** & **Channels** & **Layers** & **Stride** \\
**i** & **\(\mathbf{F_{i}}\)** & \(\mathbf{S_{i}\times T_{i}}\) & \(\mathbf{C_{i}}\) & \(\mathbf{L_{i}}\) & **Stride** \\ \hline
1 & Conv3 \(\times\) 3 & 64 \(\times\) 344 & 64 & 1 & 2 \\
2 & MBConv1,k3 & 48 \(\times\) 172 & 32 & 4 & 1 \\
3 & MBConv6,k3 & 48 \(\times\) 172 & 48 & 7 & 1 \\  & \(\times\) 3 & & & & \\
4 & MBConv6,k5 & & & & \\  & \(\times\) 5 & 48 \(\times\) 172 & 80 & 1 & 2 \\
5 & MBConv6,k5 & 24 \(\times\) 86 & 80 & 6 & 1 \\
6 & MBConv6,k3 & 24 \(\times\) 86 & 160 & 1 & 2 \\  & \(\times\) 3 & & & & \\
7 & MBConv6,k3 & & & & \\  & \(\times\) 3 & 12 \(\times\) 43 & 160 & 9 & 1 \\
8 & MBConv6,k5 & & & & \\  & \(\times\) 5 & & & & \\
9 & MBConv6,k5 & & & & \\  & \(\times\) 5 & & & & \\
10 & MBConv6,k5 & 6 \(\times\) 21.5 & 384 & 12 & 1 \\  & \(\times\) 5 & & & & \\  & \(\times\) 3 & & & & \\  & Conv\_head,k1 & 6 \(\times\) 21.5 & 2560 & 1 & 1 \\  & \(\times\) 1 & & & & \\
13 & Tower1,k3 \(\times\) & 1 \(\times\) 1 & 2 & 1 & 1 \\  & 3 & & & & \\
14 & Tower2,k1 \(\times\) & 1 \(\times\) 1 & 2 & 1 & 1 \\
15 & Skip,k1 \(\times\) 1 & 1 \(\times\) 1 & 2 & 1 & 1 \\
16 & FC & 1 \(\times\) 1 & 32 & 1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dual-Tower Network—Each row describes a stage ‘i’ with ‘\(\text{L}_{i}\)’ layers, input Mel frequency bands, time steps ¡\(\text{S}_{i},\text{T}_{i}\)¿, stride, and output channel count ‘\(\text{C}_{i}\)’. Figure 5: Architecture of the Dual-Tower Network: The blue part in Figure 5 is the EfficientNet-b7 architecture, k represents the convolution kernel size, s represents the step size, MBConv1,6 represents the expansion factor of the output channels, Depthwise represents deep separable convolution, the orange part refers to DFSM, FC stands for fully connected layer, Conv2d represents two-dimensional convolution, Bn2 represents normalization, Sigmoid and Relu are activation functions, and the gray-white part represents the Head module. resources, as well as the scaling of different network layers. EfficientNet-b7, a deep and high-performance convolutional neural network, was chosen primarily to strike a balance between model depth, computational efficiency, and accuracy. While EfficientNet-b7 indeed delivers improved accuracy, it comes at the cost of an increased number of parameters compared to smaller variants in the EfficientNet series. This often necessitates a trade-off between performance, computational complexity, and model size. In the case of Mel spectrograms converted from insect sounds, we adapt the input channels of the model's initial convolutional layer from 3 to 2 to accommodate audio Mel spectrogram input. The backbone network of EfficientNet-b7 is built by stacking MBConv structures, which comprise multiple recurrent convolutional blocks. Each convolutional block includes multiple convolution layers, batch normalization layers, and activation functions, as illustrated in Figure 6. MBConv1,6 represents the expansion factor of the output channels. Utilizing this deep architecture for extracting rich, high-level features proves instrumental in capturing complex information from insect sounds. These extracted features are subsequently fed into the DFSM for further processing and classification, enabling the network to comprehend more intricate image patterns. The squeeze-and-excitation (SE) module is an attention mechanism that comprises a global average pooling layer and two fully connected layers (as depicted in Figure 7). This module enhances the network's focus on essential features, offering channel-wise adaptive weighting to feature maps, consequently improving the model's expressiveness and performance. In the case of EfficientNet-B7, the SE module is applied to the output of each residual block [38] to heighten the network's attention to critical features, thereby further enhancing the model's accuracy. In nature, insect sounds serve various purposes, from mating and warning to navigation.