MAKAMCYCLE: IMPROVING THE UNDERSTANDING OF TURKISH MAKAM MUSIC THROUGH THE MEDIACYCLE FRAMEWORK

The goal of this project is to investigate the challenges of creating a tool to aid people of diverse profiles, from musicology experts and music information retrieval (MIR) specialists, to the interested non-technical users outside these fields in understanding traditional makam music of Turkey. We aim at providing a “playground” approach, with which MIR specialists can easily validate algorithms for feature extraction, clustering and visualization, and non-technical users can navigate by easily varying parameters and triggering audiovisual previews. We adapted the MediaCycle framework for organization of media files by similarity. AudioCycle, its audio application, allows users to cluster a large number of audio files against a subset of extracted audio features, visualized in a 2D space through positions, distances, colors. Transitions between parametric changes are animated, which helps the user create and retain a mental model of the sounds and their relationships. For our proof-of-concept, we defined our use case as detecting makamlar (plural) from makam music. We integrated the “pitch histogram” technique proposed by Bozkurt et. al as a feature extraction plugin in AudioCycle to meet this goal.