## 4 Results and Analysis
### Performance Analysis of Transformer Based on Patch-Mix and Contrastive Learning  
To verify the superiority of the proposed Patch-Mix and CL-Transformer model framework, a series of ablation experiments are meticulously designed and executed. The models used in these experiments are all pre-trained models and are fine-tuned for 50 epochs. The input features, Aug-Fbank, are enriched through the application of our combined data augmentation technique. Considering that excessive background noise can interfere with the model's learning and recognition of audio signals, while inadequate levels fail to augment the model's noise robustness, we experimentally determined the optimal signal-to-noise ratio (SNR) for the Noise-Fbank feature to be 10 dB. An effective interplay between noise reduction and model robustness is guaranteed by our novel methods.  
Table 5 showcases the profound impact of incorporating Patch-Mix, utilizing a signal-to-noise ratio (SNR) of 10 dB, into the Fbank feature representation, when coupled with our innovative combined data augmentation technique. This synergy yields outstanding accuracies of 96.5% and 91.63% across the respective datasets. It makes a noteworthy improvement of 1% and 0.71%, respectively. It is supposed to be tailored to the unique requirements of diverse application scenarios. This underscores the effectiveness of our approach in enhancing classification performance in real-world situations. When using the CL-Transformer model as the classification model and applying combined data augmentation to the original audio, the classification accuracies reach 97% and 91.87%, respectively, representing an improvement of 4.75% and 3.19% compared to the baseline model. Finally, by employing the proposed audio classification framework that combines the CL-Transformer model with Patch-Mix, along with the improved combined data augmentation scheme, classification accuracies of 97.75% and 92.95% are achieved on the ESC-50 and UrbanSound8K datasets, respectively. These achievements signify an astonishing improvement of 5.5% and 4.27% over the baseline model, reaching the highest classification accuracy. These experiments demonstrate that Patch-Mix, contrastive learning, and the improved combined data augmentation scheme all contribute to the improvement of classification accuracy.  
Figure 5 shows the classification accuracy and loss values on the training and validation sets at different epochs for the UrbanSound8K dataset. It can be clearly observed that the model reaches optimal performance after the 20 th epoch. Additionally, the accuracy on both the training and validation sets becomes stable and close to 1, indicating that the proposed method has good generalization ability. Moreover, the proposed model framework  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Technical Method** & **ESC-50 (\%)** & **UrbanSound8K (\%)** \\ \hline AST & - & 92.25 & 88.68 \\ AST & Traditional & 94.25 & 89.48 \\ AST & DRC & 94.50 & 89.73 \\ AST & DRC + TW (10) & 95.00 & 90.32 \\ AST & DRC + TW (20) & 95.50 & 90.92 \\ AST & DRC + TW (30) & 94.25 & 90.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Combined data augmentation ablation experiments.  
effectively mitigates the overfitting problem commonly encountered by pre-trained models in downstream tasks.