## 2 Related Works
### Challenges in Constructing Music Datasets for AI Research  
Constructing music datasets for AI research involves several challenges, including data collection, annotation, quality, and legal and ethical considerations [63; 64].  
Music data comes from diverse and dispersed sources, such as studio recordings, live performances, and field collections, making it difficult to centralize. Partnerships with institutions and the use of online platforms can streamline this process. Navigating copy  
Figure 3: Manchu musical instruments and performance scenes.  
right permissions is complex and time-consuming, requiring collaboration with copyright holders to clarify data usage and obtain the necessary permissions.  
Annotating music data involves capturing details like pitch, rhythm, harmony, and instrumentation, which is both labor-intensive and time-consuming. Semi-automated annotation, supported by machine learning and expert review, can improve efficiency and accuracy. Ensuring consistency across annotators is another challenge, which can be mitigated with detailed guidelines, training, and cross-validation. Rich metadata, including cultural context and performance techniques, is crucial but often hard to acquire, necessitating collaboration with subject-matter experts.  
Audio quality varies due to differences in recording equipment and environments, which can negatively impact AI model performance. Advanced audio processing techniques can enhance audio quality. Data integrity issues, such as missing or incomplete data, affect dataset usability and can be addressed through regular integrity checks and systematic data management.  
Music datasets often involve copyright-protected works, requiring adherence to legal frameworks and permissions to avoid disputes. Privacy concerns regarding performers and creators can be addressed through data anonymization. Cultural sensitivity is essential to respect traditions and customs, ensuring ethical data usage.  
Constructing music datasets involves complex tasks, such as audio feature extraction and data cleaning, requiring advanced technologies and efficient tools. Interdisciplinary collaboration among musicologists, computer scientists, and ethnologists is vital for enhancing dataset diversity and research depth. Establishing collaborative platforms can facilitate this process and ensure comprehensive dataset development.  
Addressing these challenges is crucial for developing high-quality music datasets that can effectively support AI research in music analysis, generation, and emotion recognition. Ensuring robust data collection, comprehensive annotation, and consistent quality enhance the datasets' reliability and utility. Moreover, respecting legal, ethical, and cultural considerations is vital to safeguard the rights of creators, protect privacy, and honor the cultural significance of the music. This not only ensures compliance with legal standards but also fosters trust and collaboration among stakeholders, including researchers, cultural institutions, and communities. By tackling these issues, the resulting datasets can serve as valuable resources for advancing AI applications in ethnomusicology, contributing to the preservation and dissemination of cultural heritage on a global scale.