Imposing Higher-Level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints

Abstract

We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient des- cent constraint optimisation to provide further control over the genera- tion process. Among other things, this allows for the use of a “template” piece, from which some structural properties can be extracted, and trans- ferred as constraints to the newly generated material. The sampling pro- cess is guided with Simulated Annealing to avoid local optima, and to find solutions that both satisfy the constraints, and are relatively stable with respect to the C-RBM. Results show that with this approach it is possible to control the higher-level self-similarity structure, the meter, and the tonal properties of the resulting musical piece, while preserving its local musical coherence.

How to Cite

Lattner S. , Grachten M. & Widmer G. (2018) “Imposing Higher-Level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints”, Journal of Creative Music Systems. 2(2). doi: https://doi.org/10.5920/jcms.2018.01

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Authors

Stefan Lattner (Johannes Kepler University)
Maarten Grachten (Austrian Research Institute for Artificial Intelligence)

Gerhard Widmer (Johannes Kepler University)

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Creative Commons Attribution 4.0

Peer Review

This article has been peer reviewed.

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