## 5 Experimental Results
### Case Studies on the Effectiveness of the Dataset  
To validate the effectiveness and application potential of the Manchu music dataset, this section presents several specific case studies. These studies demonstrate the dataset's performance in practical applications, including music classification, music generation, and emotion analysis. In one case study, we used the Manchu music dataset for a music classification task. The goal was to automatically classify different types of Manchu music using a deep learning model, specifically a Convolutional Neural Network (CNN). The classification results obtained from the CNN model successfully categorized different types of Manchu music segments into their respective classes. The model's performance was particularly notable in the \"folk ensemble\" category, achieving an accuracy rate of 90%. These results indicate that the rich samples provided by the Manchu music dataset effectively support the automatic classification of music genres, as shown in Table 11. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Dataset** & **Application Effect** \\ \hline Manchu music dataset & It performs well in tasks such as music classification, generation, and sentiment analysis, and can provide in-depth analysis and understanding of the characteristics of Manchu music. \\ \hline Million Song Dataset & It is widely used in research on popular music classification, recommendation systems, etc., but its support for ethnic music is weak. \\ \hline GTZAN Genre Collection & It is often used in music genre classification research, but the small scale of the dataset and low sound quality limit its application scope. \\ \hline MAESTRO Dataset & It performs well in the generation and analysis of classical music, but its support for other types of music is limited. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison results of application effects on different datasets. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Dataset** & **Performances** & \begin{tabular}{c} **Duration** \\ **(Hours)** \\ \end{tabular} & **Quality** & **Format** & **Frequency** & **Music Type** & \begin{tabular}{c} **Performing** \\ **Style** \\ \end{tabular} & \begin{tabular}{c} **Regional** \\ **Distribution** \\ \end{tabular} \\ \hline Manchu music dataset & 5000 & 500 & High & WAV & 44.10 kHz & 5 types & Diverse & China \\ \hline Million Song Dataset & 1,000,000 & 11,000 & Low & MP3 & 22.05 kHz & \
\begin{tabular}{c} Various \\ types \\ \end{tabular} & Extensive & Global \\ \hline GTZAN Genre Collection & 1000 & 100 & Medium & MP3 & 22.05 kHz & 10 types & Fixed & Unknown \\ \hline MAESTRO Dataset & 1200 & 200 & High & WAV & 44.10 kHz & 1 types & Classic &
\begin{tabular}{c} Western \\ world \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multi-dimensional comparison results of datasets. We employed a Generative Adversarial Network (GAN) for the task of generating new music pieces with characteristics of Manchu music. The generated quality received a score of 7.8 out of 10. The Frechet Audio Distance (FAD) value was 0.32. The generated music segments displayed key features of Manchu music, such as the use of specific instruments and melodic styles. Although there were slight differences in certain details compared to real music, the overall style and structure were consistent with the original music. This indicates that the GAN model can effectively utilize the dataset to generate music segments with ethnic characteristics, demonstrating practical application value. We used a Random Forest model to perform emotion analysis on the Manchu music dataset, aiming to identify the emotional types in music segments, such as \"joy\", \"sadness\", and \"calm\". We trained and tested the model on 3000 audio clips from the dataset. The emotion analysis model successfully identified the primary emotions present in the music segments.