A Transformational Modified Markov Process for Chord-Based Algorithmic Composition

Meirav Amram, Etan Fisher, Shai Gul, Uzi Vishne

Research output: Contribution to journalArticlepeer-review


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 languageAmerican English
Pages (from-to)43
Number of pages1
JournalMath. Comput. Appl.
Issue number3
StatePublished - 1 Jul 2020


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