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Journal Article

A computational cognitive model for the analysis and generation of voice leadings


Harrison,  Peter M. C.
Research Group Computational Auditory Perception, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Queen Mary University of London;

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Harrison, P. M. C., & Pearce, M. T. (2020). A computational cognitive model for the analysis and generation of voice leadings. Music Perception, 37(3), 208-224. doi:10.1525/mp.2020.37.3.208.

Cite as: https://hdl.handle.net/21.11116/0000-0006-6C39-2
Voice leading is a common task in Western music composition whose conventions are consistent with fundamental principles of auditory perception. Here we introduce a computational cognitive model of voice leading, intended both for analyzing voice-leading practices within encoded musical corpora and for generating new voice leadings for unseen chord sequences. This model is feature-based, quantifying the desirability of a given voice leading on the basis of different features derived from Huron’s (2001) perceptual account of voice leading. We use the model to analyze a corpus of 370 chorale harmonizations by J. S. Bach, and demonstrate the model’s application to the voicing of harmonic progressions in different musical genres. The model is implemented in a new R package, “voicer,” which we release alongside this paper.