## 3 Results
across all evaluation metrics, further validating the effectiveness of the social-emotional CNN model in social-emotional music classification tasks. This study investigates how different feature extraction methods affect the performance of SEM-Net. The results of the ablation experiments are summarized in Table 7. Without feature extraction, the accuracy is only 52.78%. However, after applying various feature extraction techniques, classification accuracy improves significantly. Specifically, when using MFCC, Chroma, or Spectral Contrast individually, the accuracy reaches 80.60%, 81.94%, and 60.65%, respectively, demonstrating the importance of these features in music classification. Furthermore, combining multiple features leads to additional performance improvements. For instance, MFCC + Chroma achieves 76.85%, MFCC + Spectral Contrast reaches 77.31%, while Chroma + Spectral Contrast results in 64.52%. Notably, when integrating all three features (Chroma + Spectral Contrast + MFCC), the accuracy reaches 94.13%, representing a 41.35% improvement over the 54.78% accuracy without feature extraction. Table 8 presents the classification results for three types of social-emotional music. The model performed best on empathic perspective-taking, with an accuracy of 91.94% and mAP of 97.76%, followed by outlook and problem-solving, achieving accuracy above 90% and mAP over 95%. These results show that the model can effectively distinguish between different types of social-emotional music. While the performance in the others category was slightly lower, it still maintained reasonable accuracy, indicating the model's general classification ability. \begin{table}
\begin{tabular}{c c c} \hline
**Before** & **After** \\ \hline Accuracy & 42.3107 & 89.3518 \\ \hline Recall & 35.3129 & 89.2071 \\ \hline Precision & 35.2817 & 87.3305 \\ \hline mAP & 32.6175 & 94.6216 \\ \hline \end{tabular}
\end{table} 
Table 6: SEM-Net classification comparison before and after pre-processing. \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Accuracy** & **Recall** & **Precision** & **mAP** \\ \hline Empathetic perspective-taking & 91.9355 & 97.8261 & 93.7500 & 97.7639 \\ \hline Outlook & 89.7849 & 90.1961 & 80.7018 & 96.1655 \\ \hline Problem-solving & 90.8602 & 81.3953 & 87.5001 & 95.0258 \\ \hline Others & 80.5555 & 83.9286 & 84.6847 & 84.2512 \\ \hline \end{tabular}
\end{table} 
Table 8: Three types of social-emotional music and other music classification result. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy** & **Recall** & **Precision** & **mAP** \\ \hline Original & 52.7817 & 33.3301 & 17.5909 & 33.3333 \\ \hline MFCC & 80.5957 & 78.6009 & 78.3749 & 84.2512 \\ \hline Chroma & 81.9440 & 82.4309 & 79.4573 & 88.4472 \\ \hline Spectral Contrast & 60.6481 & 55.7368 & 60.5837 & 64.8835 \\ \hline MFCC + Chroma & 76.8518 & 77.2238 & 74.9399 & 83.2052 \\ \hline MFCC + Spectral Contrast & 77.3148 & 75.9861 & 75.1992 & 87.8583 \\ \hline Chroma + Spectral Contrast & 64.5161 & 57.1965 & 64.5223 & 67.9755 \\ \hline Chroma + Spectral Contrast + MFCC & 94.1296 & 93.0114 & 93.7352 & 92.0842 \\ \hline \end{tabular}
\end{table} 
Table 7: SEM-Net feature extraction comparison. In the task of music classification, LSTM, Transformer, and BBNN represent different model strategies for feature processing. LSTM excels at handling sequential data and capturing long-term dependencies in music, making it suitable for modeling temporal features such as melody and rhythm [26]. On the other hand, Transformer utilizes a self-attention mechanism to simultaneously attend to different segments of an entire music piece, offering advantages in parallel computation and long-range dependency modeling [27]. It is particularly effective in capturing complex musical structures, though it generally demands higher computational resources and larger datasets.