AAAI Publications, Ninth Artificial Intelligence and Interactive Digital Entertainment Conference

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Tracking Creative Musical Structure: The Hunt for the Intrinsically Motivated Generative Agent
Benjamin Smith

Last modified: 2013-11-13


Neural networks have been employed to learn, generalize, and generate musical pieces with a constrained notion of creativity. Yet, these computational models typically suffer from an inability to characterize and reproduce long-term dependencies indicative of musical structure. Hierarchical and deep learning models propose to remedy this deficiency, but remain to be adequately proven. We describe and examine a novel dynamic bayesian network model with the goal of learning and reproducing longer-term formal musical structures. Incorporating a computational model of intrinsic motivation and novelty, this hierarchical probabilistic model is able to generate pastiches based on exemplars.


music; artificial intelligence; machine learning; adaptive neural network; dynamic bayesian network; reinforcement learning;

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