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Capturing the dynamics of cellular automata, for the generation of synthetic persian music, using conditional restricted Boltzmann machines

Davis, Darryl N.; Arshi, Sahar

Authors

Darryl N. Davis

Sahar Arshi



Abstract

© Springer International Publishing AG 2017. In this paper the generative and feature extracting powers of the family of Boltzmann Machines are employed in an algorithmic music composition system. Liquid Persian Music (LPM) system is an audio generator using cellular automata progressions as a creative core source. LPM provides an infrastructure for creating novel Dastgāh-like Persian music. Pattern matching rules extract features from the cellular automata sequences and populate the parameters of a Persian musical instrument synthesizer [1]. Applying restricted Boltzmann machines, and conditional restricted Boltzmann machines as two family members of Boltzmann machines provide new ways for interpreting the patterns emanating from the cellular automata. Conditional restricted Boltzmann machines are particularly employed for capturing the dynamics of cellular automata.

Citation

Davis, D. N., & Arshi, S. (2017). Capturing the dynamics of cellular automata, for the generation of synthetic persian music, using conditional restricted Boltzmann machines. In Artificial Intelligence XXXIV; Lecture Notes in Computer Science (72-86). Cambridge, UK: Springer Verlag. https://doi.org/10.1007/978-3-319-71078-5_6

Conference Name SGAI-AI 2017
Start Date Dec 12, 2017
End Date Dec 14, 2017
Acceptance Date Oct 30, 2017
Online Publication Date Nov 21, 2017
Publication Date 2017
Deposit Date Dec 11, 2017
Publicly Available Date Oct 27, 2022
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Volume 10630 LNAI
Pages 72-86
Series Title Artificial Intelligence XXXIV, Volume 10630 of the Lecture Notes in Computer Science
Series ISSN 0302-9743
Book Title Artificial Intelligence XXXIV; Lecture Notes in Computer Science
Chapter Number Six
ISBN 9783319710778; 9783319710785
DOI https://doi.org/10.1007/978-3-319-71078-5_6
Public URL https://hull-repository.worktribe.com/output/499702
Publisher URL https://link.springer.com/chapter/10.1007/978-3-319-71078-5_6

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