Comparing Feature-Based Models of Harmony

Predictive processing is a fundamental process in music cognition. While there are a number of predictive models of melodic structure, fewer approaches exist for harmony/chord prediction. This paper compares the predictive performance of n-gram, HMM, autoregressive HMMs as well as feature-based (or multiple-viewpoint) n-gram and Dynamic Bayesian Network Models of harmony, which used a basic set of duration and mode features. The evaluation was performed using a hand-selected corpus of Jazz standards. Multiple-viewpoint n-gram models yield strong results and outperform plain HMM models. However, feature-based DBNs outperform n-gram models and HMMs when incorporating the mode feature, but perform worse when duration is added to the models. Results suggest that the DBNs provide a promising route to modelling tonal harmony.

CMMR-submission 4.pdf
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In  Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval CMMR 2012

Publisher  Springer
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TypeInproceedings
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