Artificial Intelligence Agents in Music Analysis: An Integrative Perspective Based on Two Use Cases

Despite considerable technological innovation, comprehensive reviews synthesizing the application and evolution of artificial intelligence (AI) in the field of music analysis remain scarce. Although early studies on computer-assisted composition and rule-based analysis established a foundation for the automated exploration of musical form and content Hiller (1959), there is still a limited body of literature addressing the complete progression from traditional algorithms to recent AI-driven models and hybrid systems. Pioneering work such as Miranda's Miranda (2021), underscores the influence of AI, supercomputing, and evolutionary computation in shaping the first computational tools for creation. Recent reviews (Wang et al. (2024); Lerch et al. (2025)) focus on intelligent music generation systems. However, a systematic integration of these historical advances with state-of-the-art AI methodologies and musical analysis is largely absent. In the last decade, deep learning frameworks--including convolutional neural networks, recurrent neural networks, and transformer architectures--have led to breakthroughs in music information retrieval.