## 4 Discussion
For out-of-bounds values, interpolation using edge values was employed, allowing for a scientific adjustment of the array dimensions while preserving data integrity. The remaining comparative experiments involved deep learning transfer models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **F1** & **Recall** & **Precision** & **Param** & **FLOPS** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(Million)** & **(GigaFLOPs)** \\ \hline ResNet50 [42] & 63.16 & 43.73 & 47.19 & 43.97 & 25.58 & 1.82 \\ RegNet [45] & 73.68 & 52.30 & 58.80 & 52.27 & 107.84 & 14.15 \\ ConvNext [46] & 59.21 & 46.44 & 51.51 & 45.89 & 4.65 & 0.79 \\ MnasNet [47] & 52.63 & 40.27 & 44.06 & 44.30 & 5.28 & 0.15 \\ ShuffleNetv2 [48] & 73.68 & 52.36 & 55.78 & 54.08 & 2.31 & 0.07 \\ MLP-Mixer [49] & 61.84 & 42.63 & 46.72 & 46.51 & 17.90 & 3.76 \\ DenseNet201 [50] & 61.84 & 44.49 & 48.28 & 47.60 & 21.23 & 0.11 \\ MobileNetv2 [51] & 78.95 & 57.20 & 59.01 & 59.05 & 3.54 & 0.14 \\ Swin Transformer [52] & 63.16 & 50.34 & 51.87 & 54.36 & 27.54 & 4.37 \\ Dual-Tower Network & 80.26 & 62.02 & 65.68 & 65.66 & 66.39 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance Comparison of Different Models. Figure 10: A bar chart depicting the model results. This figure provides an intuitive representation of the testing performance of the ten models on InsectSet32. To gain a deeper understanding of the performance of the dual-tower network and the contributions of its components, we conducted a series of ablation experiments. In these experiments, we progressively removed different parts of the DFSM, including the DFSM itself and the two separate tower structures. Based on the experimental data presented in Table 8, we observed that removing the DFSM decreased the model's performance, resulting in a 5.26% decrease in accuracy (as shown in Table 2). All other metrics (F1, Recall, and Precision) also showed declines. This strongly indicates the substantial contribution of the DFSM to the task. Building on this discovery, we removed Tower 1 and Tower 2 to validate the importance of each tower further, both of which led to decreased model performance. When conducting generalization experiments, our focus is on verifying the performance of the dual-tower network on different datasets and its ability to generalize in practical applications. This paper selected three diverse datasets, including environmental sounds from ESC-50 [17], urban sounds from UrbanSound8K [18], and speech commands from Speech Commands [19]. Each dataset represents distinct sound backgrounds and classification tasks. This experimental design enables a comprehensive evaluation of the model's adaptability and generalizability, providing insights into its performance across various sound environments. Through a series of steps involving data preparation, model application, and performance evaluation, we achieved good results, as presented in Table 9. The dual-tower network attained an accuracy of 85.75% on the ESC-50 dataset, showcasing its capacity to recognize diverse sound categories in a natural environment and underscoring its potential to adapt to natural sound backgrounds. It demonstrated good performance on the UrbanSound8K dataset, achieving an accuracy of 97.89%, which is particularly true given the complex conditions of urban environments, including urban noise and various sound events. Furthermore, the model exhibited success on the Speech Commands dataset, with an accuracy of 93.94%, further confirming its practicality in speech command recognition and speech-to-text tasks. The outcomes of this series of experiments underscore the superior performance. Its effectiveness extends beyond insect sound classification tasks