Improving the Understanding of Turkish Makam Music through the MediaCycle Framework

The goal of this work is to investigate the challenges of creating a tool to aid people of diverse profiles, from musi- cology 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 trig- gering audiovisual previews. We adapted the MediaCycle framework for organization of media files by similarity. AudioCycle, its audio application, allows users to clus- ter 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.