## 3 Results
BBNN is a structurally flexible convolutional network that uses multi-branch and multi-scale feature extraction to fuse local and global audio features effectively [28], and CNN extracts local time-frequency features of music through convolutional layers [29]. BBNN and CNN often achieve efficient performance in classification tasks, mainly when applied to time-frequency representations like Mel-spectrograms. According to the data analysis in Table 9, the proposed model outperforms other models across all evaluation metrics, demonstrating superior classification performance. In the model comparison, the accuracy of LSTM and Transformer models was 42.86% and 38.33%, respectively, indicating their suboptimal performance in this study's application scenario. This may be attributed to LSTM's reliance on long-sequence temporal information and the Transformer's insufficient training data, which may have hindered its ability to learn musical features thoroughly. Additionally, the BBNN model achieved a relatively higher accuracy of 67.38%, yet it still fell short of the proposed method. The CNN + LSTM model achieved an accuracy of 53.59%, slightly outperforming LSTM alone but remaining below an ideal level. In contrast, the proposed method significantly outperformed all other models, achieving an accuracy of 94.13%, recall of 93.01%, precision of 93.74%, and mAP of 92.08%. To validate the superiority of the proposed approach, k-fold cross-validation (k = 10) was conducted, ensuring fairness in comparison by applying the same hyperparameter settings across different models. Furthermore, a statistical significance test was performed on the results, revealing that the proposed method achieved a \(p\)-value < 0.05 compared to other models, indicating that its advantage is statistically significant. These findings confirm the effectiveness of the proposed SEM-Net model in this application domain, demonstrating its suitability for social-emotional music classification compared to traditional models. Among all the models, the Transformer model has the highest number of parameters, reaching 350,274, which reflects the high computational resource demand caused by its multi-head attention mechanism and deep stacking architecture. In contrast, the LSTM model has the fewest parameters, only 145,411, indicating a more straightforward structure suitable for deployment in resource-constrained embedded systems. The SEM-Net model has 251,091 parameters, which is higher than that of LSTM (145,411) and BBNN (206,714) but lower than CNN + LSTM (267,500) and Transformer (350,274). SEM-Net maintains a reasonable level of parameter efficiency while still delivering strong performance without introducing excessive computational overhead. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **mAP** & **Parameter Quantity** \\ \hline LSTM [26] & 42.8601 & 35.5647 & 40.0017 & 42.4727 & 145,411 \\ \hline Transformer [27] & 38.3347 & 27.7685 & 51.4485 & 32.3627 & 350,274 \\ \hline BBNN [28] & 67.7438 & 67.3816 & 68.7674 & 73.5882 & 206,714 \\ \hline CNN + LSTM [29] & 53.8759 & 53.5971 & 61.9795 & 68.0274 & 267,500 \\ \hline SEM-Net (this study) & 94.1296 & 93.0114 & 93.7352 & 92.0842 & 521,091 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison with different classification models.