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Title:Music composition using recurrent neural networks and evolutionary algorithms
Author(s):Pelletier, Calvin
Subject(s):artificial intelligence
music composition
machine learning
artificial neural network
evolutionary algorithm
Abstract:The ability to generate original music is a significant milestone for the application of artificial intelligence in creative fields. This paper explores two techniques for autonomous music composition: recurrent neural networks and evolutionary algorithms. Both methods utilize data in the Nottingham Music Database of folk songs in ABC music notation. The neural network inputted and outputted individual ASCII characters and quickly learned to generate valid ABC notation from training on the dataset. The fitness function for the evolutionary algorithm was evaluated by comparing various characteristics of the generated song to target characteristics calculated using the dataset. Both methods are capable of composing homophonic music consisting of a monophonic melody and a chord progression
Issue Date:2017-12
Date Available in IDEALS:2018-03-15

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