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.

Algorithms and technology have so far helped listeners to more of the same music. Now, UiO researchers are working on new technology that can get people interested in a greater musical variety. Chords, beat, timbre, rhythm and harmony. All these elements of music contribute to make it sound the way it does. But have you thought about why you like particular kinds of music?

The overarching goal of music theory is to explain the inner workings of a musical composition by examining the structure of the composition. Schenkerian music theory supposes that Western tonal compositions can be viewed as hierarchies of musical objects. The process of Schenkerian analysis reveals this hierarchy by identifying connections between notes or chords of a composition that illustrate both the small- and large-scale construction of the music. We present a new probabilistic model of this variety of music analysis, details of how the parameters of the model can be learned from a corpus, an algorithm for deriving the most probable analysis for a given piece of music, and both quantitative and human-based evaluations of the algorithm's performance. This represents the first large-scale data-driven computational approach to hierarchical music analysis.

We present a novel, real-time system for exploring harmonic spaces of musical styles, to generate music in collaboration with human performers utilizing gesture devices (such as the Kinect) together with MIDI and OSC instruments / controllers. This corpus-based environment incorporates statistical and evolutionary components for exploring potential flows through harmonic spaces, utilizing power-law (Zipf-based) metrics for fitness evaluation. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the Classical Music Archives 14,000+ MIDI corpus, among others. The user interface supports real-time exploration of the balance between predictability and surprise for musical composition and performance, and may be used in a variety of musical contexts and applications.