Since the early 20th century, various theories have been advanced in order to mathematically explain and notate modes of Traditional Turkish music known as maqams. In this article, maqam scales according to various theoretical models based on different tunings are compared with pitch measurements obtained from select recordings of master Turkish performers in order to study their level of match with analysed data. Chosen recordings are subjected to a fully computerized sequence of signal processing algorithms for the automatic determination of the set of relative pitches for each maqam scale: f0 estimation, histogram computation, tonic detection þ histogram alignment, and peak picking. For nine well-recognized maqams, automatically derived relative pitches are compared with scale tones defined by theoretical models using quantitative distance measures. We analyse and interpret histogram peaks based on these measures to find the theoretical models most conforming with all the recordings, and hence, with the quotidian performance trends influenced by them.

A method for hierarchical classification of makams from symbolic data is presented. A makam generally implies a miscellany of rules for melodic composition using a given scale. Therefore, makam detection is to some level similar to the key detection problem. The proposed algorithm classifies makams by applying music theoretical knowledge and statistical evidence in a hierarchical manner. The makams using similar scales are first grouped together, and then identified in detail later. The first level of the hierarchical decision is based on statistical information provided by the n-gram likelihood of the symbolic sequences. A cross-entropy based metric, perplexity, is used to calculate similarity between makam models and the input music piece. Later, using statistical features related to the content of the piece, such as the tonic note, the average pitch level for local excerpts and the overall pitch progression, a more detailed identification of the makam is achieved. Different length n-grams and representation paradigms are used, including the Arel theory, the 12 tone equal tempered representation, and interval contour. Results show that the hierarchical approach is better, compared to a straightforward n-gram classification, for the makams which have similar pitch space, such as Hüseyni-Muhayyer and Rast-Mahur. Using the proposed methodology, the system's recall rate increases from 88.7% to 90.9% where there exists still some confusion between the makams Uşşak and Beyati.

ÖzetçeBu çalıúma bilgi eriúimi uygulamaları açısından Türk müzi÷inin Batı müzi÷i ile farklılıklarını tartıúmaya açmaktadır. Türk müzi÷i bilgi eriúimi için frekans histogramı kullanımını önermekte ve otomatik karar sesi tespiti, makam sınıflandırma, ses sistemi analizi, kuram -icra uyuúma düzeyinin ölçülmesi gibi uygulamalar için geliútirilmiú bir dizi aracı içeren Makam Aracı (Makam Toolbox) 1.0'ın ve beraberinde büyük bir parametrik veritabanının tanıtımını yapmaktadır. AbstractThis study discusses differences between Turkish music and Western music from an information retrieval perspective. It proposes use of frequency histograms for various music information retrieval applications: automatic tonic detection, makam classification, tuning analysis, theory-practice mismatch measurement. It announces a Matlab toolbox: Makam Toolbox 1.0 and a parametric database for Turkish music information retrieval research.978-1-4244-4436-6/09/$25.00 ©2009 IEEE 978-1-4244-4436-6/09/$25.00 ©2009 IEEE

Automatic melodic segmentation is a topic studied extensively, aiming at developing systems that perform grouping of musical events. Here, we consider the problem of automatic segmentation via supervised learning from a dataset containing segmentation labels of an expert. We present a statistical classification-based segmentation system developed specifically for Turkish makam music. The proposed system uses two novel features, a makam-based and an usul-based feature, together with features commonly used in literature. The makam-based feature is defined as the probability of a note to appear at the phrase boundary, computed from the distributions of boundaries with respect to the piece's makam pitches. Likewise, the usul-based feature is computed from the distributions of boundaries with respect to beats in the rhythmic cycle, usul of the piece. Several experimental setups using different feature groups are designed to test the contribution of the proposed features on three datasets. The results show that the new features carry complementary information to existing features in the literature within the Turkish makam music segmentation context and that the inclusion of new features resulted in statistically significant performance improvement.

Bu çalı mada, literatürde ilk defa klasik Türk müzi i için bir notaya dökme sistemi sunulmaktadır. Önce Türk müzi inin, sistem tasarımında dikkate alınan, Batı müzi inden farklı özellikleri özetlenmektedir. Daha sonra, sistemi olu turan i aret i leme adımları olan; ses i aretinden frekans ölçümü, frekans da ılımlarından otomatik karar sesi frekans tespiti ve makam tanıma, frekans bilgisinin aralık bilgisine dönü türülmesi, frekans ve süre nicemleme adımları, birbiriyle ili kili bir ekilde özetlenerek ve sistem içerisindeki fonksiyonları açıklanarak sunulmu tur.