## 3 Method
### Data Processing  
Data processing is a critical step in ensuring the high quality and practicality of the Manchu music dataset, as shown in Table 1.  
Audio data processing includes audio cleaning, segmentation, feature extraction, and format conversion. Audio cleaning involves the use of noise reduction algorithms to remove background noise from recordings and standardize volume levels, thereby ensuring consistency and clarity in the audio. Audio segmentation employs audio segmentation algorithms for automatic clipping, supplemented by manual corrections based on professional auditory assessments to ensure the accuracy of the segments. In the domain of feature extraction, techniques such as Fast Fourier Transform (FFT) are utilized for spectral analysis to extract spectral features, as well as time-domain features and higher-level musical characteristics. All audio files are uniformly converted to formats such as WAV or FLAC, with various sampling rates provided to meet different application needs. Video data processing involves video editing, enhancement, and audio-video synchronization. Automated editing software is employed to identify and extract key segments of music performances; these clips are subsequently refined by professionals to ensure the video's conciseness and focus. Video enhancement is achieved through image processing algorithms that improve clarity and color, as well as repair and optimize low-quality videos, with frame rates adjusted to ensure smooth playback. Audio-video synchronization utilizes audio recognition and alignment algorithms, accompanied by manual validation from professionals to ensure high precision in the synchronization of audio and video. Metadata processing is another crucial aspect of data processing, involving the standardization, automatic annotation, and manual review of metadata. Metadata is formatted uniformly in structures such as JSON or XML, clearly defining each field and encompassing performer information, types of instruments, and performance techniques. Key data extracted include the title of the music piece, composer details, recording date and location, duration, genre, and specific cultural or historical context. Machine learning and natural language processing (NLP) techniques are employed for the preliminary extraction and annotation of metadata and enhancing annotation efficiency, and dedicated metadata annotation tools are developed to support bulk annotation and automatic corrections. The \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Operation** & **Method** \\ \hline \multirow{4}{*}{Audio data processing} & Audio cleaning & Noise removal; Volume standardization \\  & Audio segmentation & Automatic segmentation; Manual correction \\  & Feature extraction & Spectrum analysis; Time domain features; High-level features \\  & Format conversion & Uniform format; Multiple sampling rates \\ \hline \multirow{3}{*}{Video data processing} & Video editing & Automatic editing; Manual editing \\  & Video enhancement & Image optimization; Frame rate adjustment \\  & Audio and video synchronization & Automatic synchronization; Manual proofreading \\ \hline \multirow{3}{*}{Metadata processing} & Standardization & Metadata format; Field definition \\  & Automatic labeling & Preliminary annotation; Annotation tool \\  & Manual review & Expert review; Consistency check \\ \hline \multirow{3}{*}{Data storage and management} & Database design & Relational database; File storage system \\  & Data backup & Multiple backups; Off-site backup \\  & Data access & API interface; User permission management \\ \hline \multirow{3}{*}{Data processing challenges and solutions} & Large data volume & High cost; Improve data processing efficiency and storage capacity \\  & Various data quality & Diverse data sources; Formulate strict data processing standards \\  & Complex processing & Technical complexity; Setup interdisciplinary teams, collaborate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data processing task list.