## 2 Materials and Methods
### Social-Emotional Music Classification Neural Network Architecture
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Activation** & **Filter Number/Filter Size/Stride** \\ \hline Image Input & & Size: 250 \(\times\) 200 \(\times\) 3 \\ \hline Convolution\_1 & ReLU & 32 Filters/5 \(\times\) 5/Stride 1 \\ \hline Batch Normalization & & Normalizes activations to stabilize training \\ \hline MaxPooling\_1 & & Pool size: 2 \(\times\) 2 \\ \hline Dropout\_1 & & Rate: 0.25 \\ \hline Convolution\_2 & ReLU & 64 Filters/5 \(\times\) 5/Stride 1 \\ \hline Batch Normalization & & Normalizes activations to reduce internal covariate shift \\ \hline MaxPooling\_2 & & Pool size: 2 \(\times\) 2 \\ \hline Dropout\_2 & & Rate: 0.3 \\ \hline Convolution\_3 & ReLU & 128 Filters/5 \(\times\) 5/Stride 1 \\ \hline Batch Normalization & & Normalizes activations to ensure stable gradients during backpropagation \\ \hline MaxPooling\_3 & & Pool size: 2 \(\times\) 2 \\ \hline Dropout\_3 & & Rate: 0.35 \\ \hline Flatten & & Converts 2D feature maps to 1D vector for fully connected layers \\ \hline Dense\_1 & ReLU & Units: 256 \\ \hline Dropout\_4 & & Rate: 0.4 \\ \hline Dense\_2 (Output) & Softmax & Units: Number of Classes \\ \hline \end{tabular}
\end{table} 
Table 1: SEM-Net architecture. The music dataset used in this study was annotated with the assistance of professional music scholars and special education experts. The size of the dataset is shown in Table 3, consisting of approximately 200 music pieces categorized into three types, each averaging around 2 min and 30 s in length. Data augmentation techniques were applied to enhance the reliability and stability of the deep learning model, including random noise addition, playback speed adjustment (ranging from 0.8\(\times\) to 1.2\(\times\)), and time shift modifications to the audio. The augmented dataset increased to five times the original dataset, producing 980 music pieces. The augmented dataset was then split into training and validation sets at a ratio of 8.5:1.5, with 210 music reserved for testing. Accuracy, recall, precision, and mean average precision (mAP) are the key metrics for evaluating the model's performance. The calculations for these metrics are defined in (4), (5), (6), and (7). The confusion matrix is a widely used tool for evaluating the performance of classification models, particularly in multi-class classification tasks, as shown in Table 4. \begin{table}
\begin{tabular}{c c} \hline Initial Learning Rate & 0.001 \\ MaxEpoch & 300 \\ BatchSize & 32 \\ Learning Rate Drop Factor & 0.1 \\ LearnRateDropPeriod & 10 \\ Shuffle & Every-epoch \\ \hline \end{tabular}
\end{table} 
Table 2: The HyperParameter value used in the deep learning. Figure 4: SEM-Net layer connection diagram. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{Before Data Augmentation} \\ \hline Set & Training Set & Validation Set & Test Set & Total \\ \hline Total & 130 & 24 & 42 & 196 \\ \hline \multicolumn{5}{c}{After Data Augmentation} \\ \hline Set & Training Set & Validation Set & Test Set & Total \\ \hline Empathetic perspective-taking & 250 & 50 & 75 & 375 \\ \hline Outlook & 210 & 40 & 70 & 320 \\ \hline Problem-solving & 190 & 30 & 65 & 285 \\ \hline Total & 650 & 120 & 210 & 980 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Music dataset comparison before and after data augmentation. It provides a matrix representation of the relationship between predicted and actual labels, allowing for an analysis of the model's performance across different classes. The confusion matrix is typically an n\(\times\)n matrix, where n represents the total number of classes. Each element within the matrix indicates the correspondence between the model's predictions and the actual labels. The confusion matrix compares predicted values with actual values, presenting results in terms of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).