## 4 Discussion  
This preliminary investigation aims to contribute to the growing literature on enhancing social-emotional learning for individuals with special needs through an innovative AI-based music classification model. The strong performance of SEM-Net is closely linked to the integration of appropriate data preprocessing techniques and complementary audio feature extraction methods. Specifically, time stretching, pitch shifting, and noise augmentation introduced controlled variability into the input data, which enhanced the model's ability to generalize across diverse musical expressions. Three feature extraction methods--MFCC, Chroma, and Spectral Contrast--were selected for their ability to capture different yet complementary aspects of musical content. MFCC captures timbral characteristics, Chroma reflects pitch and harmonic relationships, and Spectral Contrast emphasizes variations in energy across frequency bands. As shown in Table 7, although each feature individually achieved reasonable accuracy, their combination significantly improved overall performance, reaching 94.13%. This demonstrates that these features complement each other effectively and enable SEM-Net to capture the nuanced patterns in social-emotional music better. Thus, this study has three contributions: (1) Proposed an innovative social-emotional music classification model,\" SEM-Net\", to help those people who need special assistance. (2) Through the social-emotional pre-processing and feature extraction method, SEM-Net can achieve the highest accuracy of 94.13%. (3) Compared with traditional models such as LSTM or Transformer, SEM-Net achieves at least 20% higher classification accuracy. Although its parameter count is slightly higher than parts of traditional models, SEM-Net demonstrates a more favorable balance between model complexity and performance. This demonstrates the effectiveness of the proposed method in accurately categorizing social-emotional music.  
However, this study has certain limitations. The first limitation lies in the dataset used for training and evaluation. While the model performs well within the scope of the selected dataset, it does not comprehensively represent all relevant music genres or styles that may influence social-emotional learning. As a result, its applicability may be restricted to specific scenarios, and further work is needed to expand and diversify the dataset to improve generalizability. The second limitation concerns the scalability of the proposed model. Although the model demonstrates strong classification capabilities, its ability to process large-scale music databases in real-world applications remains uncertain. Future research should enhance computational efficiency and optimize the model for large-scale, real-time music classification tasks. This could involve exploring self-supervised learning techniques, data augmentation strategies, and more efficient deep learning architectures to improve performance and efficiency.  
Future studies should investigate how social-emotional music classification can be integrated into personalized learning environments or therapeutic applications. One possible direction is to develop an adaptive AI system that can dynamically adjust to the emotional needs of individuals by recommending suitable social-emotional music. Additionally, incorporating multimodal data (e.g., physiological signals, facial expressions, or behavioral responses) could further refine the model's ability to interpret emotional states and provide more personalized feedback. In summary, while this study presents a promising approach to social-emotional music classification, further research is necessary to address dataset limitations, enhance model scalability, and explore its integration into real-world applications. Future advancements in deep learning, affective computing, and personalized AI systems will play a crucial role in enhancing the practical impact of AI-driven social-emotional learning tools.