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Capturing the Dynamics of Cellular Automata, for the Generation of Synthetic Persian Music, Using Conditional Restricted Boltzmann Machines

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Artificial Intelligence XXXIV (SGAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10630))

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Abstract

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.

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Authors and Affiliations

  1. School of Engineering and Computer Science, University of Hull, Hull, UK

    Sahar Arshi & Darryl N. Davis

Authors
  1. Sahar Arshi
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  2. Darryl N. Davis
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Correspondence to Sahar Arshi .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. Middlesex University , London, United Kingdom

    Miltos Petridis

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Arshi, S., Davis, D.N. (2017). Capturing the Dynamics of Cellular Automata, for the Generation of Synthetic Persian Music, Using Conditional Restricted Boltzmann Machines. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-71078-5_6

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