## 2 Related Works  
### Existing Music Datasets and Their Applications  
With advancements in AI technology, music datasets have become fundamental resources for tasks in music information retrieval, recommendation systems, automated composition, and emotion analysis [24; 25; 26; 27; 28; 29; 30; 31; 32]. Prominent datasets, including the Million Song Dataset (MSD) [33], GTZAN Genre Collection [19], MagnaTagATune [34], Nsynth [35], and the Free Music Archive (FMA) Dataset [36], have underpinned various applications such as music classification, recommendation, and automated generation. These datasets provide essential frameworks for building AI models capable of recognizing musical features, predicting user preferences, and generating compositions, which collectively support the broader field of music informatics.  
Music recommendation systems, for instance, utilize collaborative filtering, content-based approaches, and hybrid algorithms to deliver tailored song suggestions based on users' listening patterns and preferences. Music information retrieval (MIR) [37] relies on audio feature extraction techniques to enable efficient search and categorization by analyzing attributes like melody, rhythm, and timbre. In automated music composition, deep learning models, including Generative Adversarial Networks (GANs) [38] and Recurrent Neural Networks (RNNs) [39], are trained on datasets to capture compositional structures and patterns, allowing them to generate new music pieces with coherent style and flow [40]. Additionally, emotion analysis combines musical features such as pitch, rhythm, and harmony with labeled emotional data to train AI models that can classify and generate music according to specific emotional tones, enhancing user engagement and experience [41; 42].  
Despite their utility, existing music datasets present limitations, particularly in the context of ethnomusicology and the preservation of cultural heritage. Major datasets are often biased towards Western popular music, leading to gaps in the representation of traditional and ethnic music forms. These datasets frequently lack diversity, with limited audio and metadata annotations that fail to capture the cultural context or performance nuances unique to traditional music. Issues such as data duplication, erroneous tagging, and inconsistent audio quality further hinder their effectiveness, especially in applications requiring rich, culturally diverse datasets. Moreover, the scale and depth of these datasets are often insufficient for training AI models intended for the comprehensive analysis of non-Western and traditional music genres.  
To address these gaps, the construction of specialized datasets, such as an AI-ready collection focused on ethnic music like Manchu music, is essential. By incorporating detailed annotations that highlight cultural context, musical characteristics, and historical background, such datasets can enhance AI's ability to process and interpret ethnic music forms accurately. This focus on diversity and representation not only broadens the scope of AI applications in music analysis but also plays a crucial role in preserving intangible cultural heritage by providing a structured, digital record of traditional music for future generations.