## 6 Discussion  
### Insights Gained from Experiments  
The insights gained from the experiments indicate that the Manchu music dataset performs exceptionally well across various AI tasks, particularly in music classification, generation, and emotion analysis. The dataset's high quality, diversity, and specificity provide a solid foundation for model training and application, while also offering valuable references for future research and optimization.  
During the experiments, we found that the high audio quality of the dataset significantly improved the classification and generation performance of the models. The  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classification** & **A** & **P** & **R** & **F1** \\ \hline CNN & 85.7\% & 82.3\% & 84.1\% & 83.2\% \\ Traditional instrument solo & 86.9\% & 83.4\% & 83.2 & 83.3\% \\ Folk ensemble & 90.0\% & 85.9\% & 80.5 & 83.1\% \\ Folk song singing & 83.6\% & 80.3\% & 86.7 & 83.5\% \\ Festival music & 82.3\% & 80.1 & 85.2 & 82.7\% \\ Religious ceremony music & 85.7 & 81.8\% & 84.9 & 83.4\% \\ \hline \end{tabular}
\end{table}
Table 11: Classification results of the test dataset under the CNN model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Emotional Type** & **A** & **P** & **R** & **F1** \\ \hline Random Forest & 83.4\% & 81.0\% & 82.5\% & 81.7\% \\ Joy & 87.0\% & 83.4\% & 80.2 & 81.8\% \\ Sadness & 79.0\% & 78.4\% & 86.5 & 82.3\% \\ Clam & 84.2\% & 81.2\% & 80.8 & 81.0\% \\ \hline \end{tabular}
\end{table}
Table 12: Emotional type results of the test dataset under the Random Forest model.  
diversity of the dataset also played a crucial role in enhancing the models' generalization capabilities and robustness. In the music generation tasks, both the Generative Adversarial Network (GAN) and Transformer models showed excellent performance, though further optimization is needed for generating finer details and maintaining style consistency. The Convolutional Neural Network (CNN) demonstrated high accuracy in classification tasks, while the Transformer model outperformed other models in music generation tasks, capturing complex audio features and long-term dependencies.  
Compared to large-scale music datasets like the Million Song Dataset, the Manchu music dataset, despite being smaller in scale, offers unique value in the study of ethnic music due to its specific focus. The high audio quality of the dataset enhances model performance in analysis tasks, providing more accurate feature extraction and higher quality analysis results. This finding underscores the importance of high-quality data for model training and analysis outcomes. With high-quality audio data and rich musical samples, researchers can better understand and analyze the characteristics, styles, and emotional expressions of Manchu music, thereby advancing ethnic music research.  
Future research can leverage this dataset for more in-depth studies on ethnic music, exploring topics such as the evolution of musical styles and regional differences. The experimental results also reveal the strengths and weaknesses of different models in handling specific tasks, offering directions for future model optimization. For instance, improvements can be made in handling the details of music generation with GAN models, or further enhancing the accuracy of models in emotion analysis. These insights help us better understand the practical application potential of the dataset and provide direction for future research efforts.