Self-organizing maps
A Hierarchical Self-Organizing Map Model for Sequence Recognition
Neural Processing Letters
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
On Intelligence
Real-Time Pitch Spelling Using the Spiral Array
Computer Music Journal
Towards music fitness evaluation with the hierarchical SOM
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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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.