A Computational Cognitive Model for the Analysis and Generation of Voice Leadings

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.

[1]  David Huron,et al.  Voice Leading: The Science behind a Musical Art , 2016 .

[2]  David Huron,et al.  Tone and Voice: A Derivation of the Rules of Voice-Leading from Perceptual Principles , 2001 .

[3]  Marcus T. Pearce,et al.  The construction and evaluation of statistical models of melodic structure in music perception and composition , 2005 .

[4]  Marcus T. Pearce,et al.  An Energy-based Generative Sequence Model for Testing Sensory Theories of Western Harmony , 2018, ISMIR.

[5]  Dmitri Tymoczko,et al.  The Geometry of Musical Chords , 2006, Science.

[6]  William P. Birmingham,et al.  Algorithms for Chordal Analysis , 2002, Computer Music Journal.

[7]  Richard Parncutt,et al.  AN IMPROVED MODEL OF TONALITY PERCEPTION INCORPORATING PITCH SALIENCE AND ECHOIC MEMORY , 1993 .

[8]  Wolfram Menzel,et al.  HARMONET: A Neural Net for Harmonizing Chorales in the Style of J. S. Bach , 1991, NIPS.

[9]  Peter M C Harrison,et al.  Simultaneous Consonance in Music Perception and Composition , 2019, Psychological review.

[10]  Masuzo Yanagida,et al.  A modular system generating Jazz-style arrangement for a given set of a melody and its chord name sequence , 2008 .

[11]  William Hutchinson,et al.  The acoustic component of western consonance , 1978 .

[12]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[13]  Craig Stuart Sapp.  Online Database of Scores in the Humdrum File Format , 2005, ISMIR.

[14]  D. Shanahan,et al.  Diachronic Changes in Jazz Harmony: A Cognitive Perspective , 2013 .

[15]  Kemal Ebcioglu,et al.  An Expert System for Harmonizing Four-Part Chorales , 1988, ICMC.

[16]  David Huron,et al.  Critical Bands and the Spelling of Vertical Sonorities , 1992 .

[17]  Albert S. Bregman,et al.  The Auditory Scene. (Book Reviews: Auditory Scene Analysis. The Perceptual Organization of Sound.) , 1990 .

[18]  Darrell Conklin,et al.  Representation and Discovery of Vertical Patterns in Music , 2002, ICMAI.

[19]  Dominik Hörnel,et al.  ChordNet: Learning and Producing Voice Leading with Neural Networks and Dynamic Programming , 2004 .

[20]  Claire Arthur,et al.  The direct octaves rule: Testing a scene-analysis interpretation , 2016 .

[21]  L. Trainor,et al.  Explaining the high voice superiority effect in polyphonic music: Evidence from cortical evoked potentials and peripheral auditory models , 2014, Hearing Research.

[22]  Jose D. Fernández,et al.  AI Methods in Algorithmic Composition: A Comprehensive Survey , 2013, J. Artif. Intell. Res..