A Transformational Modified Markov Process for Chord-Based Algorithmic Composition

Meirav Amram; Etan Fisher; Shai Gul; Uzi Vishne

doi:10.3390/mca25030043

A Transformational Modified Markov Process for Chord-Based Algorithmic Composition

Meirav Amram
, Etan Fisher
, Shai Gul
, Uzi Vishne

Department of Mathematics

Bar-Ilan University

Research output: Contribution to journal › Article › peer-review

Abstract

The goal of this research is to maximize chord-based composition possibilities given
a relatively small amount of information. A transformational approach, based in group theory,
was chosen, focusing on chord intervals as the components of a modified Markov process. The Markov
process was modified to balance between average harmony, representing familiarity, and entropy,
representing novelty. Uniform triadic transformations are suggested as a further extension of the
transformational approach, improving the quality of tonality. The composition algorithms are
demonstrated given a short chord progression and also given a larger database of albums by the
Beatles. Results demonstrate capabilities and limitations of the algorithms.

Original language	English
Pages (from-to)	43
Number of pages	1
Journal	Mathematical and Computational Applications
Volume	25
Issue number	3
DOIs	https://doi.org/10.3390/mca25030043
State	Published - 1 Jul 2020

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A Transformational Modified Markov Process for Chord-Based Algorithmic Composition

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