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

Ella Gale; Oliver Matthews; Ben De Lacy Costello; Andrew Adamatzky

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

Ella Gale^*
, Oliver Matthews
, Ben De Lacy Costello
, Andrew Adamatzky

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

3 Citations (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 language	English
Title of host publication	Music and Unconventional Computing - AISB Convention 2013
Pages	28-49
Number of pages	22
Publication status	Published - 2013
Event	Music and Unconventional Computing, Held at the AISB Convention 2013 - Exeter, United Kingdom Duration: 3 Apr 2013 → 5 Apr 2013

Conference

Conference	Music and Unconventional Computing, Held at the AISB Convention 2013
Country/Territory	United Kingdom
City	Exeter
Period	3/04/13 → 5/04/13

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

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.",

author = "Ella Gale and Oliver Matthews and \{De Lacy Costello\}, Ben and Andrew Adamatzky",

year = "2013",

language = "English",

isbn = "9781908187345",

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note = "Music and Unconventional Computing, Held at the AISB Convention 2013 ; Conference date: 03-04-2013 Through 05-04-2013",

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T1 - Beyond Markov chains, towards adaptive memristor network-based music generation

AU - Gale, Ella

AU - Matthews, Oliver

AU - De Lacy Costello, Ben

AU - Adamatzky, Andrew

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/84894215199

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:84894215199

SN - 9781908187345

SP - 28

EP - 49

BT - Music and Unconventional Computing - AISB Convention 2013

T2 - Music and Unconventional Computing, Held at the AISB Convention 2013

Y2 - 3 April 2013 through 5 April 2013

ER -

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

Abstract

Conference

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