## 4 Experimental Setup
### Baseline Experiment Description  
The baseline experiment is a critical step in evaluating the effectiveness and practical value of the Manchu music dataset. By establishing baseline experiments, we can define the fundamental performance metrics of various models and algorithms when processing this dataset, providing a reference for subsequent optimization. This section details the design and implementation of the baseline experiments, including the objectives, data partitioning, experimental setup, performance metrics, and result analysis. 1. The primary objectives of the baseline experiments are as follows:  
To validate the quality of the dataset: Assessing the quality, integrity, and usability of the audio and video files in the dataset. To evaluate model performance: Assessing the performance of different AI models and algorithms on the Manchu music dataset, providing a reference for further optimization and improvement. Figure 8: Music classification and sentiment analysis network framework diagram. To explore the dataset's application potential: Investigating the potential applications of the dataset in tasks such as music classification, feature extraction, and emotion analysis. 2. To ensure the reliability and reproducibility of the experimental results, the dataset is divided according to the following principles: Training Set: Used for model training, comprising 70% of the total dataset. Validation Set: Used for tuning and selecting models, comprising 15% of the total dataset. Test Set: Used for the final evaluation of model performance, comprising 15% of the total dataset. Random Partitioning: Data are randomly partitioned based on the number and types of music segments, ensuring diversity and representativeness of music genres and performance styles across all subsets. Stratified Sampling: In addition to random partitioning, stratified sampling is employed to ensure that the data distribution in each subset is consistent with the original dataset. 3. The specific setup for the baseline experiments includes the following: Hardware Environment: Experiments are conducted on a high-performance computing cluster equipped with NVIDIA GPUs and large storage capacity. Software Environment: The experiments use Python programming language, deep learning frameworks (PyTorch), audio processing libraries (Librosa), and data analysis tools (Pandas). Model Selection: Feature Extraction Models: MFCC, LPC, STFT Classification Models: CNN, RNN, SVM Generative Models: GAN, VAE, Transformer Emotion Analysis Models: Random Forest, k-NN Experimental Procedures: Data Preprocessing: Includes denoising, segmentation, and feature extraction from audio and video files. Model Training: Training models using the training set, adjusting hyperparameters, and optimizing model performance. Model Validation: Validating models using the validation set, selecting the best model and parameter settings. Model Testing: Conducting the final evaluation using the test set and recording the model's performance metrics across different tasks. 4. To comprehensively evaluate model performance, the baseline experiments use the following metrics: Accuracy (A): The accuracy of classification models on the test set, reflecting the overall classification capability of the model. Precision (P): The precision of the model in specific categories, indicating the accuracy of the model's recognition in those categories. Recall (R): The recall of the model in specific categories, indicating the model's coverage in recognizing those categories. F1 Score (F1): The harmonic mean of precision and recall, providing a comprehensive measure of the model's performance in specific categories. Generation Quality (GQ): The quality of music segments generated by generative models, evaluated through subjective assessments and objective metrics such as audio similarity. Processing Time (PT): The time taken by the model during training, validation, and testing phases, indicating the computational efficiency of the model. 5.