## 3 Results  
This section analyzes the classification performance of the developed CNN model for social-emotional music. Figure 5a,b illustrate the model's performance during the training and validation processes, where the blue line represents training accuracy, and the orange line represents validation accuracy. According to the experimental results, the model achieved 90% training accuracy at epoch 180 and continued to converge between epochs 180 and 300, reaching the highest validation accuracy of 92.12% at epoch 280. Simultaneously, the validation loss remained stable during this period, indicating that the model did not exhibit significant overfitting. The final social-emotional music confusion matrix, presented in Figure 6, demonstrates that most social-emotional music samples were accurately classified into their respective categories. This study employs repeated validation with ten iterations to ensure the stability and reliability of the model. After discarding the two worst-performing results, the average of the remaining results is taken as the final accuracy metric. This approach effectively reduces variations caused by weight initialization, stochastic batch training, and data partitioning, mitigating the uncertainty introduced by a single training instance in model evaluation. The experimental results are shown in Table 5 and indicate that the proposed CNN model achieves an average accuracy of 89.69%, with a maximum accuracy of 92.13%. Additionally, the model attains an average recall of 88.70% (maximum 90.06%), an average precision of 88.81% (maximum 90.74%), and an average mean average precision (mAP) of 93.95% \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Real**} \\ \cline{3-4}  & & **True** & **False** \\ \hline \multirow{3}{*}{Predicted} & True & \begin{tabular}{c} Tp \\ (True Positive) \\ \end{tabular} & \begin{tabular}{c} Fp \\ (False Positive) \\ \end{tabular} \\ \cline{2-4}  & False & \
\begin{tabular}{c} Fn \\ (False Negative) \\ \end{tabular} &
\begin{tabular}{c} Tn \\ (True Negative) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confuse matrix, which is often used to evaluate CNN’s performance. (maximum 94.72%). These results demonstrate the model's stability and effectiveness in social-emotional music classification tasks. Furthermore, we analyze the performance of different proposed social-emotional CNN models before and after applying the social-emotional music preprocessing method. As shown in Table 6, the accuracy before preprocessing was only 42.31%, whereas the proposed method improved accuracy to 89.35%, representing an approximate 57% increase, demonstrating significant performance enhancement. Additionally, recall increased from 35.31% to 89.21%, precision increased from 35.28% to 87.31%, and mAP rose from 32.61% to 94.62%. These results indicate that the proposed method achieves substantial improvements  
Figure 5: Social–emotional CNN training process in 300 epochs. (**a**) SEM-Net accuracy training process. (**b**) SEM-Net loss function training process. Figure 6: SEM-Net confuse matrix. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Round** & **Accuracy** & **Recall** & **Precision** & **mAP** \\ \hline Round 1 & 87.6344 & 87.4898 & 86.1046 & 89.4479 \\ \hline Round 2 & 89.3518 & 89.2071 & 87.3309 & 94.6216 \\ \hline Round 3 & 90.8602 & 89.2508 & 89.9715 & 94.5757 \\ \hline Round 4 & 90.3225 & 89.3749 & 88.31713 & 94.5399 \\ \hline Round 5 & 87.0967 & 85.9624 & 85.4076 & 95.2868 \\ \hline Round 6 & 89.7849 & 88.7694 & 89.4971 & 94.1361 \\ \hline Round 7 & 88.1720 & 86.8567 & 86.9572 & 92.9899 \\ \hline Round 8 & 87.6344 & 87.4898 & 87.4456 & 93.9839 \\ \hline Round 9 & 89.2473 & 88.6984 & 87.8575 & 94.7236 \\ \hline Round 10 & 92.1296 & 90.0116 & 90.7352 & 92.0842 \\ \hline Average & 90.6878 & 88.7073 & 88.5140 & 93.9568 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ten-fold cross-validation.