Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object Citation - WoS: 2Türk Makam Müziği Notaları için Otomatik Ezgi Bölütleme(Institute of Electrical and Electronics Engineers Inc., 2014) Bozkurt, Barış; Karaçalı, Bilge; Karaosmanoğlu, M. Kemal; Ünal, ErdemAutomatic 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 makant melodies and usul 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.Conference Object Citation - Scopus: 6Music Information Retrieval for Turkish Music: Problems, Solutions and Tools(Institute of Electrical and Electronics Engineers Inc., 2009) Bozkurt, Barış; Gedik, Ali Cenk; Karaosmanoğlu, M. KemalBu çalışma bilgi erişimi uygulamaları açısından Türk müziğinin Batı müziği ile farklılıklarını tartışmaya açmaktadır. Türk müziği bilgi erişimi için frekans histogramı kullanımını önermekte ve otomatik karar sesi tespiti, makam sınıflandırma, ses sistemi analizi, kuram – icra uyuşma düzeyinin ölçülmesi gibi uygulamalar için geliştirilmiş bir dizi aracı içeren Makam Aracı (Makam Toolbox) 1.0’ın ve beraberinde büyük bir parametrik veritabanının tanıtımını yapmaktadır.Article Citation - WoS: 32Citation - Scopus: 61Three Dimensions of Pitched Instrument Onset Detection(Institute of Electrical and Electronics Engineers Inc., 2010) Holzapfel, Andre; Bozkurt, Barış; Stylianou, Yannis; Gedik, Ali Cenk; Gedik, Ali Cenk; Bozkurt, BarışIn this paper, we suggest a novel group delay based method for the onset detection of pitched instruments. It is proposed to approach the problem of onset detection by examining three dimensions separately: phase (i.e., group delay), magnitude and pitch. The evaluation of the suggested onset detectors for phase, pitch and magnitude is performed using a new publicly available and fully onset annotated database of monophonic recordings which is balanced in terms of included instruments and onset samples per instrument, while it contains different performance styles. Results show that the accuracy of onset detection depends on the type of instruments as well as on the style of performance. Combining the information contained in the three dimensions by means of a fusion at decision level leads to an improvement of onset detection by about 8% in terms of F-measure, compared to the best single dimension. © 2010 IEEE.