## 5 Experimental Results
### The Performance of AI Models Using the Dataset  
This section provides a detailed report on the performance of various AI models when applied to the Manchu music dataset. We evaluated the models across different tasks, including classification, generation, regression, and sentiment analysis, assessing their accuracy, efficiency, and generation quality. In the music classification task, we tested the following models: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Support Vector Machines (SVM). The classification tasks included both music genre classification and emotion classification. CNN: CNNs demonstrated excellent performance in extracting audio features and classifying music genres. They achieved high accuracy and F1 scores, particularly when handling complex audio features and multi-class problems. RNN: RNNs performed well in processing time-series data, such as music sequences. However, their accuracy and F1 scores were slightly lower than those of CNNs, possibly due to limitations in learning long sequences. SVM: SVMs performed adequately on small-scale datasets, but their performance decreased when handling larger datasets. They were less effective in dealing with complex music features. As shown in Table 5, the comparative performance metrics highlight the strengths and weaknesses of each model in different aspects of music classification. In the music generation task, we utilized Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. The tasks included generating new Manchu music segments and style transfer, as shown in Table 6.  
GANs were capable of producing high-quality audio when generating new music segments, achieving a high similarity to real music. However, some generated music pieces may exhibit minor differences from real music in certain details. VAEs performed well in generating diverse music, although the quality was slightly lower compared to GANs. VAEs are suitable for generating music segments with some variation. Transformer models excelled at generating music segments with complex structures, receiving the highest generation quality scores. They were able to produce high-quality audio that closely resembles real music. In the feature extraction and regression tasks, we tested feature extraction methods such as Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & \
\begin{tabular}{c} **Generating a Quality** \\ **Score(10)** \\ \end{tabular} &
\begin{tabular}{c} **Frechet Audio Distance** \\ **(FAD)** \\ \end{tabular} \\ \hline GAN & 7.8 & 0.32 \\ VAE & 7.2 & 0.38 \\ Transformer & 8.1 & 0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison table of Manchu music generation performance of different models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **A** & **P** & **R** & **F1** \\ \hline CNN & 85.7\% & 82.3\% & 84.1\% & 83.2\% \\ RNN & 80.5\% & 78.0\% & 79.8\% & 78.9\% \\ SVM & 75.2\% & 72.0\% & 73.5\% & 72.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Music classification performance comparison table of different models. Short-Time Fourier Transform (STFT), and evaluated their performance in audio feature regression tasks. MFCC performed exceptionally well in both audio feature extraction and regression tasks, effectively capturing the spectral characteristics of music. It is useful for subsequent music analysis and generation. LPC exhibited stable performance in extracting audio features, but its error rate was slightly higher compared to MFCC. This may be due to LPC's limitations in handling complex audio signals. STFT effectively extracted the time-frequency characteristics of audio, with performance close to that of MFCC. However, its longer processing time may impact the efficiency of handling large-scale datasets.