## 2 Related Works
### Research on Traditional Chinese Music Datasets  
Traditional Chinese music, a significant part of world music culture, has a long history and diverse forms [43]. While modern technology has made great strides in developing datasets for Western popular music, research on traditional Chinese music datasets remains relatively limited. Existing datasets primarily focus on three categories: Chinese classical music, Chinese folk music, and Chinese opera.  
Chinese classical music datasets feature recordings of traditional instruments such as guqin, guzheng, and pipa [44; 45]. These high-quality recordings capture intricate performance techniques and are widely used in tasks like instrument recognition, music style classification, and music generation. Chinese folk music datasets cover the music of various ethnic groups, such as Han, Zhuang, and Miao, collected from field recordings and cultural heritage practitioners. They are instrumental for studies in ethnic music research, cultural preservation, and emotion analysis [46; 47]. Chinese opera music datasets, which include recordings of Peking Opera, Yue Opera, and Huangmei Opera, document vocal techniques, performance movements, and stage settings, supporting opera performance analysis and music classification [48; 49].  
Significant progress has been made in applying these datasets to areas like instrument recognition, style classification, music generation, and emotion analysis. For instance, deep learning models such as CNNs have achieved high-accuracy instrument recognition, while GANs and VAEs have been used to generate new compositions in traditional Chinese styles, including guqin and folk songs [50; 51]. Emotion analysis models, leveraging audio feature extraction, have been developed to classify and generate music with different emotional states.  
However, these datasets face challenges such as limited coverage, incomplete annotations, variable data quality, and insufficient interdisciplinary collaboration. Many datasets focus on well-known instruments and genres, neglecting less common instruments and regional music. The lack of detailed metadata--covering aspects like instrument usage, performance techniques, and emotion tags--reduces their usability for AI training. Furthermore, variable audio quality and a lack of cultural context in annotations hinder comprehensive research and interdisciplinary applications.  
To address these issues, this study proposes constructing a dedicated Manchu music dataset. This dataset will systematically collect and annotate Manchu music resources to ensure comprehensive coverage, detailed metadata, and high-quality audio processing. Interdisciplinary collaboration with experts in musicology, ethnology, and cultural heritage preservation will enrich its cultural content, providing a solid foundation for AI research in ethnic music.  
The Manchu music dataset will not only advance intelligent music analysis and generation technologies but also contribute to the digital preservation and dissemination of Manchu music, promoting cultural heritage preservation and transmission. This effort offers significant academic and societal value, fostering a deeper understanding and appreciation of traditional Chinese music.