Computational Modelling of Harmony

Many computational models for processing music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. In this talk I describe two recent approaches to modelling musical harmony 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, metric 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 humans.