Abstract
•We combine machine learning and optimization techniques to generate music.•We study novel ways of using a Markov model to construct an objective function.•A Markov model is combined with repetitive and cyclic aspects of a music template.•We show the effectiveness of the methods on Ethiopian bagana songs.
In this research, a system is built that generates bagana music, a traditional lyre from Ethiopia, based on a first order Markov model. Due to the size of many datasets it is often only possible to get rich and reliable statistics for low order models, yet these do not handle structure very well and their output is often very repetitive. A first contribution of this paper is to propose a method that allows the enforcement of structure and repetition within music, thus handling long term coherence with a first order model. The second goal of this research is to explain and propose different ways in which low order Markov models can be used to build quality assessment metrics for an optimization algorithm. These are then implemented in a variable neighborhood search algorithm that generates bagana music. The results are examined and thoroughly evaluated.