## 4 Results and Analysis
### The Impact of the Classification Model Framework on Model Performance  
The classification model framework presented in this manuscript ingeniously incorporates Patch-Mix as the feature fusion module, the CL-Transformer model as the cornerstone for classification module. They are vividly combined with each other according to our novel combined data augmentation strategy. To verify the performance of this classification model framework, a comparative analysis is conducted against several state-of-the-art models, utilizing the publicly available ESC-50 and UrbanSound8K datasets as benchmarks. The outcomes of these experiment results, summarized in Table 6 below, provide key evidence of our method's superiority. The baseline model AST, after fine-tuning, achieved classification accuracies of 92.25% and 88.68% on the ESC-50 and UrbanSound8K datasets, respectively. The CL-Transformer \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Model** & **Method** & **ESC-50 (\%)** & **UrbanSound8K (\%)** \\ \hline DenseNet [22] & DenseNet-201 & 91.25 & 85.31 \\ SoundCLR [12] & ResNet + Hybrid Loss & 93.40 & 86.49 \\ AST [20] & Pre-trained Transformer & 95.60 & - \\ Transformers [15] & AST + Adamax & 95.80 & 89.80 \\ EAT [36] & End-to-end & 96.30 & 90.00 \\ HTS-AT [37] & Hierarchical Transformer & 97.00 & - \\ CL-Transformer & Patch-Mix + Sup-cont & 97.75 & 92.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative experiments of different classification model frameworks. Figure 5: Evaluation of accuracy and loss on training data and validation data. (**a**) Accuracy evaluation; (**b**) Loss Evaluation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Architecture** & **Loss** & **Input Feature** & **SNR (dB)** & **Mix** & **ESC-50 (\%)** & **UrbanSound (\%)** \\ \hline AST & CE & Fbank & - & - & 92.25 & 88.68 \\ AST & CE & Aug-Fbank & - & - & 95.50 & 90.92 \\ AST & CE & Aug-Fbank, Fbank & - & Patch & 94.25 & 90.32 \\ AST & CE & Noise-Fbank & 0 & Patch & 94.00 & 89.61 \\ AST & CE & Aug-Fbank, & 10 & Patch & 96.50 & 91.63 \\ CL-Transformer & CE + CL & Aug-Fbank & - & - & 97.00 & 91.87 \\ CL-Transformer & CE + CL & Aug-Fbank, & 10 & Patch & 97.75 & 92.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments based on Patch-Mix and contrastive learning. classification model framework proposed in this paper have achieved classification accuracies of 97.75% and 92.95% on the ESC-50 and UrbanSound8K datasets, respectively, representing improvements of 5.5% and 4.27% over the baseline model and outperforming some current mainstream classification schemes. Figures 6 and 7 vividly illustrate the confusion matrices of our novel CL-Transformer model applied to the ESC-50 and UrbanSound8K sets, respectively. In the figures, the horizontal and vertical coordinates correspond to the audio category labels. From Figure 6, it can be seen that the proposed method achieves 100% accuracy for some typical sound categories in urban environments, such as animal calls, wind noise, thunder, and sirens. From Figure 7, it is evident that the model maintains high classification accuracy across all categories included in the UrbanSound8K dataset. Figure 6: Confusion matrix of the CL-Transformer framework on the ESC-50 validation set. To enhance visualization, we mapped the label values on the horizontal and vertical axes to the abbreviations of each category.