Abstract
One of the elements in human music creativity results from certain features in the brain that allows it to make predictions of events based on information learnt from past music experiences. Inspired by the Memory Prediction Framework (MPF) theory, we propose a method to learn and generate new melodies based on the MPF concept. We first show how an MPF-inspired Hierarchical Self Organizing Map (MPF-HSOM) is used to capture these important features of the brain in the perspective of MPF. This MPF-HSOM is then trained with a selection of melodies taken from a corpus of folk melodies. We then show that by using a prediction algorithm, we are able to generate new melodies based on the trained MPF-HSOM of old melodies. The system proposed here is an abstraction of the features of the brain according to MPF. The results indicate that the system is able to learn and to produce novel melodies of reasonable quality.
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Law, E.HH., Phon-Amnuaisuk, S. (2012). Learning and Generating Folk Melodies Using MPF-Inspired Hierarchical Self-Organising Maps. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_37
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DOI: https://doi.org/10.1007/978-3-642-34859-4_37
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