Algorithmically generating music using specialized algorithms is a growing focus in computer science. The success of these specialized algorithms in generating music, however, depends heavily on the fitness function that is used to score the generated music and equally as important is how the fitness function is designed. Artificial intelligence in the computational composition can use certain feature set values derived from melodic analysis to serve as criteria for these fitness functions. This study explores two methods in defining the key features to be used as fitness criteria for algorithmic music generation of music that can be considered under a mix of two musical genres or hybrid-genre music. The jSymbolic tool was used to extract 101 features from musical pieces that fall under two genres. This was then reduced to a smaller feature set for use as fitness criteria. Two methods for feature reduction was explored; a decision-tree-based technique and a high-correlation-filtering technique. The study was able to confirm that each technique can be used to compose hybrid-genre music with 86% success-rate as confirmed by SVM when validated under the same dataset used in the study. This study does not claim to consistently result in a high success rate for all existing datasets.
['Aran V. Samson', 'Andrei D. Coronel']
Evolutionary algorithm-based composition of hybrid-genre melodies using selected feature sets