Automatic melodic segmentation of Turkish makam music scores

Abstract

Automatic melodic segmentation is one of the important steps in computational analysis of melodic content from symbolic data. This widely studied research problem has been very rarely considered for Turkish makam music. In this paper we first present test results for state-of-the-art techniques from literature on Turkish makam music data. Then, we present a statistical classification-based segmentation system that exploits the link between makam melodies and usual and makam scale hierarchies together with the well-known features in literature. We show through tests on a large dataset that the proposed system has a higher accuracy.