Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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Language: search.filters.language.enSubject: search.filters.subject.Turkish music

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  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    A Computational Analysis of Turkish Makam Music Based on a Probabilistic Characterization of Segmented Phrases
    (Taylor and Francis Ltd., 2015) Bozkurt, Barış; Karaçalı, Bilge
    This study targets automatic analysis of Turkish makam music pieces on the phrase level. While makam is most simply defined as an organization of melodic phrases, there has been very little effort to computationally study melodic structure in makam music pieces. In this work, we propose an automatic analysis algorithm that takes as input symbolic data in the form of machine-readable scores that are segmented into phrases. Using a measure of makam membership for phrases, our method outputs for each phrase the most likely makam the phrase comes from. The proposed makam membership definition is based on Bayesian classification and the algorithm is specifically designed to process the data with overlapping classes. The proposed analysis system is trained and tested on a large data set of phrases obtained by transferring phrase boundaries manually written by experts of makam music on printed scores, to machine-readable data. For the task of classifying all phrases, or only the beginning phrases to come from the main makam of the piece, the corresponding F-measures are.52 and.60 respectively.
  • Article
    Citation - WoS: 13
    Citation - Scopus: 20
    Weighing Diverse Theoretical Models on Turkish Maqam Music Against Pitch Measurements: a Comparison of Peaks Automatically Derived From Frequency Histograms With Proposed Scale Tones
    (Taylor and Francis Ltd., 2009) Bozkurt, Barış; Yarman, Ozan; Karaosmanoğlu, M. Kemal; Akkoç, Can
    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 + 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.
  • Article
    Citation - WoS: 36
    Citation - Scopus: 64
    Pitch-Frequency Histogram-Based Music Information Retrieval for Turkish Music
    (Elsevier Ltd., 2010) Gedik, Ali Cenk; Bozkurt, Barış
    This study reviews the use of pitch histograms in music information retrieval studies for western and non-western music. The problems in applying the pitch-class histogram-based methods developed for western music to non-western music and specifically to Turkish music are discussed in detail. The main problems are the assumptions used to reduce the dimension of the pitch histogram space, such as, mapping to a low and fixed dimensional pitch-class space, the hard-coded use of western music theory, the use of the standard diapason (A4=440 Hz), analysis based on tonality and tempered tuning. We argue that it is more appropriate to use higher dimensional pitch-frequency histograms without such assumptions for Turkish music. We show in two applications, automatic tonic detection and makam recognition, that high dimensional pitch-frequency histogram representations can be successfully used in Music Information Retrieval (MIR) applications without such pre-assumptions, using the data-driven models. © 2009 Elsevier B.V. All rights reserved.