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.

Keywords:
perception, auditory scene analysis, computational models, artificial intelligence, composition
© 2020 by The Regents of the University of California
2020
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