Machine Learning

Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints

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    Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints

    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 descent constraint optimization to provide further control over the generation process. Among other things, this allows for the use of a “template” piece, from which some structural properties can be extracted, and transferred as constraints to newly generated material. The sampling process is guided with Simulated Annealing in order to avoid local optima, and 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, as well as tonal properties of the resulting musical piece while preserving its local musical coherence.

    Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints
    by Stefan Lattner, Maarten Grachten, Gerhard Widmer
    https://arxiv.org/pdf/1612.04742v3.pdf

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