## 4 Experimental Setup  
### AI Models and Algorithms for Testing the Dataset  
To validate and evaluate the effectiveness and utility of the constructed Manchu music dataset, this study employs various AI models and algorithms for experimental testing. These models span audio processing, music information retrieval, music generation, and classification, providing a comprehensive assessment of the dataset's performance and potential in different application scenarios. 1. Audio Processing and Feature Extraction:  
Mel-frequency Cepstral Coefficients (MFCC): MFCC is a commonly used audio feature extraction technique that captures the main characteristics of an audio signal by calculating its spectral features. In this experiment, MFCC is used to extract the primary frequency components from Manchu music audio, analyzing the characteristic differences across different music genres and performance styles. Linear Predictive Coding (LPC): LPC models the generation process of an audio signal to extract parameters reflecting its structural characteristics. It is applied to analyze and compare the audio structural features of different Manchu music segments, providing a deeper understanding of the inherent properties of the audio signals. Short-Time Fourier Transform (STFT): STFT divides the audio signal into small segments and performs Fourier Transform on each segment to obtain time-frequency features. In this study, STFT is used to analyze the time-frequency characteristics of Manchu music, identifying rhythm and melody changes in the music. The feature extraction framework is shown in Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline Music Types & Folk Songs, Dance Music, Ceremonial Music \\ Number of Tracks & 500+ \\ Average Duration & 3–7 min \\ Instrumentation & Bili, Sheng, Erhu, Pipa, etc. \\ Recording Locations & Various regions in Northeast China \\ Cultural Context & Rituals, Festivals, Daily Life \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Manchu music dataset key characteristics. 2. Music Information Retrieval (MIR): Convolutional Neural Network (CNN): CNN is a deep learning model proficient in the feature extraction and classification of images and audio signals. In the experiment, CNN is used for the classification and retrieval of Manchu music segments, automatically identifying music genres and performers based on audio features. Recurrent Neural Network (RNN): RNN is suitable for handling sequential data, capturing temporal dependencies within sequences. This study uses RNN to analyze the temporal features of Manchu music, recognizing repetitive patterns and rhythmic structures. Autoencoder: An autoencoder is an unsupervised learning model that learns a low-dimensional representation of data through encoding and decoding. It is applied in this study for feature dimensionality reduction and compression, enhancing the efficiency of music retrieval and classification. The music information retrieval algorithm framework is shown in Figure 6.  
Figure 5: Audio processing and feature extraction framework diagram. Music Generation and Synthesis:  
Generative Adversarial Network (GAN): GAN generates new data resembling real data distribution through adversarial training between a generator and a discriminator. In this experiment, GAN is used to generate new Manchu music segments, demonstrating the dataset's application potential in music generation. Variational Autoencoder (VAE): VAE learns the latent representation of data through variational inference and generates new data. In this experiment, VAE is used for style transfer and variation in Manchu music, generating diverse music segments. Transformer: Based on the attention mechanism, the Transformer model is widely used in natural language processing and sequence generation tasks. This study utilizes the Transformer to generate complex melodies and harmonies in Manchu music.