Mathematical and Computational Applications (Jul 2020)

A Transformational Modified Markov Process for Chord-Based Algorithmic Composition

  • Meirav Amram,
  • Etan Fisher,
  • Shai Gul,
  • Uzi Vishne

DOI
https://doi.org/10.3390/mca25030043
Journal volume & issue
Vol. 25, no. 3
p. 43

Abstract

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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.

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