An expert system for harmonizing Chorales in the style of J. S. Bach
Understanding music with AI
An expert system for harmonizing analysis of tonal music
Understanding music with AI
PAL: A Pattern-Based First-Order Inductive System
Machine Learning - special issue on inductive logic programming
Representation and Discovery of Vertical Patterns in Music
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Algorithms for Chordal Analysis
Computer Music Journal
Functional Harmonic Analysis Using Probabilistic Models
Computer Music Journal
Modeling Meter and Harmony: A Preference-Rule Approach
Computer Music Journal
Genre classification using chords and stochastic language models
Connection Science - Music, Brain, Cognition
Robust modeling of musical chord sequences using probabilistic N-grams
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Genre classification of music by tonal harmony
Intelligent Data Analysis - Machine Learning and Music
Simultaneous estimation of chords and musical context from audio
IEEE Transactions on Audio, Speech, and Language Processing
Characterisation of composer style using high-level musical features
Proceedings of 3rd international workshop on Machine learning and music
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
Classification accuracy is not enough
Journal of Intelligent Information Systems
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Many computational models of music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. We describe two recent approaches to modelling musical harmony, using a probabilistic and a logic-based framework respectively, which attempt to reduce the gap between computational models and human understanding of music. The first is a chord transcription system which uses a high-level model of musical context in which chord, key, metrical position, bass note, chroma features and repetition structure are integrated in a Bayesian framework, achieving state-of-the-art performance. The second approach uses inductive logic programming to learn logical descriptions of harmonic sequences which characterise particular styles or genres. Each approach brings us one step closer to modelling music in the way it is conceptualised by musicians.