### Need for Dedicated AI Datasets in Ethnic Music Preservation  
Despite the cultural richness and historical significance of Manchu music, this unique musical tradition is notably underrepresented in today's digital archives and databases.  
Existing music datasets, which are predominantly designed around Western classical or popular genres, fall short in capturing the diversity and specificity required for the study of ethnic and traditional music forms like those of the Manchu. This lack of representation in digital resources poses a significant barrier to research and preservation efforts, limiting our ability to document, analyze, and promote the legacy of Manchu music using modern technological tools [23].  
In the context of AI, datasets are foundational to model development and application. AI technologies such as machine learning and deep learning rely heavily on large, high-quality datasets for tasks such as classification, generation, and emotional analysis. However, without datasets that accurately represent the distinct features of Manchu music--including its tonalities, rhythms, instrumentation, and cultural context--AI models cannot adequately process or interpret this type of music. The absence of comprehensive datasets tailored to ethnic music restricts the scope of AI research in ethnomusicology, preventing the development of models capable of analyzing and generating ethnic music forms with authenticity and cultural sensitivity.  
This study aims to address these challenges by constructing a dedicated, high-quality dataset specifically for Manchu music. The dataset is carefully curated to include a wide range of Manchu music forms, from folk songs and dance music to ceremonial pieces, each with detailed annotations that capture both musical characteristics and cultural significance. Each piece in the dataset is documented with metadata on historical context, performance style, and traditional instrumentation, ensuring that the AI models trained on these data can understand and replicate the nuances unique to Manchu music. By providing comprehensive annotations, this dataset facilitates a deeper AI-driven analysis, enabling applications that go beyond simple music categorization to more complex tasks like music generation and emotion recognition, where context and cultural background are essential for accuracy.  
The creation of this dataset represents a significant step toward bridging the gap between cultural heritage preservation and technological innovation. By offering an AI-ready resource that encompasses the unique qualities of Manchu music, this dataset contributes to the long-term digital preservation of Manchu cultural heritage. It allows researchers to apply AI techniques to explore Manchu music in new ways, such as identifying distinctive musical patterns, generating music in traditional Manchu styles, and analyzing emotional expressions embedded within the music. Furthermore, this resource opens new possibilities for interactive and educational applications, allowing a global audience to experience and learn about Manchu music within its cultural framework.  
Ultimately, this study contributes not only to the preservation and accessibility of Manchu music but also to the advancement of AI applications in ethnomusicology. It establishes a framework for how ethnic music can be systematically documented and studied using AI, demonstrating the potential of technology to support and revitalize intangible cultural heritage. This initiative thus sets a precedent for future efforts in preserving and promoting ethnic music traditions, fostering a richer, more diverse understanding of global musical heritage in the digital age.