Beyond Markov chains, towards adaptive memristor network-based music generation

Ella Gale, Oliver Matthews, Ben de Lacy Costello, Andrew Adamatzky

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

We undertook a study of the use of a memristor network for music generation, making use of the memristor's memory to go beyond the Markov hypothesis. Seed transition matrices are created and populated using memristor equations, and which are shown to generate musical melodies and change in style over time as a result of feedback into the transition matrix. The spiking properties of simple memristor networks are demonstrated and discussed with reference to applications of music making. The limitations of simulating composing memristor networks in von Neumann hardware is discussed and a hardware solution based on physical memristor properties is presented.

Original languageEnglish
Pages (from-to)181-197
Number of pages17
JournalInternational Journal of Unconventional Computing
Volume10
Issue number3
Publication statusPublished - 2014

Keywords

  • Computer music
  • Markov chain
  • Memristor
  • Memristor networks
  • Music generation
  • Neuromorphic computing

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