Composing music with complex networks

Xiaofan Liu, Chi Kong Tse, Michael Small

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.
Original languageEnglish
Title of host publicationComplex Sciences - First International Conference, Complex 2009, Revised Papers
Pages2196-2205
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 1 Dec 2009
Event1st International Conference on Complex Sciences: Theory and Applications, Complex 2009 - Shanghai, China
Duration: 23 Feb 200925 Feb 2009

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
NumberPART 2
Volume5 LNICST
ISSN (Print)1867-8211

Conference

Conference1st International Conference on Complex Sciences: Theory and Applications, Complex 2009
Country/TerritoryChina
CityShanghai
Period23/02/0925/02/09

ASJC Scopus subject areas

  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Composing music with complex networks'. Together they form a unique fingerprint.

Cite this