## 4 Discussion  
The learning rate is a hyperparameter used to update weights during the gradient descent process. In this regard, we conducted a comparative experiment to determine the optimal initial learning rate, as presented in Table 5.  
The feature extraction module is employed to reduce the dimensionality of certain raw input data or restructure the original features for subsequent use. Its primary function is to decrease data dimensionality and arrange existing data features. We compared the feature extraction module we utilized with several other classical feature extraction modules, and the results of different feature extraction modules are presented in Table 6. The recall is only 0.38% away from the best performance, surpassing the second-best performance on this dataset by 0.36%. However, our model did not perform well in terms of precision, with a 2.5% difference from the best precision. The main reason is the potential similarity between categories of insects, rendering their sound features more challenging to distinguish. Additionally, as a feature extractor, EfficientNet has fewer parameters than other feature networks, and b7 outperforms b0-b6, making it better suited for capturing local features in insect sound data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **F1** & **Recall** & **Precision** & **Param** & **FLOPS** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(GigaFLOPs)** \\ \hline Resnet [42]+DFSM & 67.11 & 47.63 & 49.74 & 52.12 & 23.54 & 1.82 \\ Vgg16 [43]+DFSM & 60.53 & 44.98 & 50.78 & 46.12 & 138.42 & 6.83 \\ Vit [44]+DFSM & 60.53 & 43.37 & 45.62 & 45.33 & 85.82 & 16.86 \\ Efficientnet-b0 [24]+DFSM & 76.32 & 56.64 & 59.48 & 60.46 & 5.31 & 0.02 \\ Efficientnet-b1 [24]+DFSM & 77.63 & 58.24 & 60.57 & 64.31 & 7.81 & 0.02 \\ Efficientnet-b2 [24]+DFSM & 75.00 & 55.66 & 58.65 & 56.82 & 9.13 & 0.02 \\ Efficientnet-b3 [24]+DFSM & 76.32 & 63.73 & 66.04 & 68.12 & 12.26 & 0.03 \\ Efficientnet-b4 [24]+DFSM & 73.68 & 60.89 & 66.06 & 64.07 & 19.37 & 0.03 \\ Efficientnet-b5 [24]+DFSM & 78.95 & 60.90 & 63.70 & 61.23 & 30.42 & 0.05 \\ Efficientnet-b6 [24]+DFSM & 77.63 & 57.42 & 60.21 & 59.93 & 43.08 & 0.06 \\ Efficientnet-b7 [24]+DFSM & 80.26 & 62.02 & 65.68 & 65.66 & 66.39 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results (% for Accuracy, F1, and Recall) for Different Feature Extraction Modules. The best, second-best, and third-best results are highlighted in red, blue, and green, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Cid** & **Species** & **Cid** & **Species** & **Cid** & **Species** & **Cid** \\ \hline _Azimada zuikensis_ & 0 & _Myposula machinlavi_ & 8 & _Platypleura chulgata_ & 16 & _Platypleura sp10_ & 24 \\ _Bretisiana brevis_ & 1 & _Myposula melanobasis_ & 9 & _Platypleura chulgata_ & 17 & _Platypleura sp11 chrititempis_ & 25 \\ _Chrithypos lipstitutants_ & 2 & _Myposula exergrasilia_ & 10 & _Platypleura sp12 chrititempis_ & 26 \\ _Chrithypos brunnes_ & 3 & _Nemobus systetisIn this study, we conducted a performance comparison of various models, including ResNet50 [42], RegNet [45], ConvNext [46], MnasNet [47], ShuffleNetV2 [48], MLP-Mixer [49], DenseNet201 [50], MobileNetV2 [51], Swin transformer [52], and our dual-tower network (as presented in Table 7) and visualized the results using bar charts (as illustrated in Figure 10). During the training of the MLP-Mixer [49] and Swin transformer [52] models, the Mel spectrogram input for insect sound conversion was [10; 2; 64; 344], while the model expected input in the shape of [10; 2; 224; 224]. To address this, we applied array sampling operations using a bilinear sampling algorithm with the \"\(align_{c}\)_orners_\" set to false. This ensured that input and output tensors were aligned at their corner pixels (as demonstrated in Figure 11).