ÖVÜNÇ ÖZTÜRK, Didem ABİDİN, Tuğba ÖZACAR

USING CLASSIFICATION ALGORITHMS FOR TURKISH MUSIC MAKAM RECOGNITION

Turkish Music pieces are used in various studies including makam recognition in computational music domain. Turkish Music pieces offer a rich content to the researchers because of their different makam properties. SymbTr is one of the most referred Turkish Music data sets in this area. In this study, the pieces from SymbTr data set belonging to 13 makams are used to execute 10 different machine learning algorithms for makam recognition and the performances of these algorithms are evaluated. These algorithms were executed on WEKA application environment and the performances in makam recognition were obtained with F-measure and recall metrics. The machine learning algorithms performed between 82% and 88%.

Klasik Türk Müziğinde Makam Tanıma İçin Veri Madenciliği Kullanımı

Türk Müziği eserleri veri kümeleri hesaplamalı müzik alanında başta makam tanıma çalışmaları olmak üzere çeşitli araştırmalarda kullanılmaktadır. Türk Müziği eserleri, farklı makamsal özellikler göstermeleri bakımından araştırmacılara zengin bir içerik sunmaktadır. Bu alanda en çok referans gösterilen Türk Müziği veri setlerinden biri SymbTr veri setidir. Bu çalışmada, SymbTr veri kümesinden 13 makama ait eserler üzerinde 10 farklı makine öğrenmesi algoritması çalıştırılmış ve bu algoritmaların performansları değerlendirilmiştir. Bu algoritmalar WEKA uygulama ortamı üzerinde çalıştırılarak makam tanımadaki başarım yüzdeleri f-ölçütü ve duyarlılık metrikleri üzerinden hesaplanmıştır. Makine öğrenmesi algoritmaları, %82-%88 arası performans göstermiştir.

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