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

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

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.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: First AISB symposium on Music and Unconventional Computing
• Start Date: Apr 2, 2013
• End Date: Apr 5, 2013
• Publication Date: Apr 2, 2013
• Publicly Available Date: Jun 24, 2019
• Peer Reviewed: Peer Reviewed
• Keywords: music, memristor, memristor network, graph theory, rock'n'roll, jazz, light opera, procedural generation of music
• Public URL: https://uwe-repository.worktribe.com/output/1435810
• Related Public URLs: http://arxiv.org/abs/1302.0785
• Additional Information: Additional Information : Oliver Matthews can be contacted at: [e-mail]; Title of Conference or Conference Proceedings : First AISB symposium on Music and Unconventional Computing; Corporate Creators : Oliver Matthews