Weighing Diverse Theoretical Models on Turkish<b><i>Maqam</i></b>Music Against Pitch Measurements: A Comparison of Peaks Automatically Derived from Frequency Histograms with Proposed Scale Tones

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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.

Section: Background On Makam Musicmentioning

confidence: 99%

A Hierarchical Approach to Makam Classification of Turkish Makam Music, Using Symbolic Data

Unal

Bozkurt

Karaosmanoğlu

2014

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“…Tura (1988) also discusses this issue in detail. using these intervals (an example is shown in Figure 2) and the scales are shown to be composed of these chords as in Figure 5 It is well known today that this tuning theory does not involve some of the very important intervals in practice (as also shown computationally in (Bozkurt, Yarman, Karaosmanoğlu, & Akkoç, 2009)). The number of intervals and notes necessary to represent the practice is an open topic of research.…”

Section: Tuning and Notationmentioning

confidence: 99%

Computational Analysis of Turkish Makam Music: Review of State-of-the-Art and Challenges

Bozkurt

Ayangil

Holzapfel

2014

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This text targets a review of the computational analysis literature for Turkish makam music, discussing in detail the challenges involved and presenting a perspective for further studies. For that purpose, the basic concepts of Turkish makam music and the description of melodic, rhythmic and timbral aspects are considered in detail. Studies on tuning analysis, automatic transcription, automatic melodic analysis, automatic makam and usul detection are reviewed. Technological and data resource needs for further advancement are discussed and available sources are presented. Osmanlı/Türk (sanat) musikisi') and exclude folk and popular genres, while most of our discussions would still find some relevance for these excluded genres. When we provide analysis results to demonstrate an approach, the data sets used include pieces from a time span of 3-4 centuries from composers like Itri, İsmail Dede Efendi, Hacı Arif Bey, Tanburi Cemil Bey and Sadettin Kaynak. 2 We use the spelling "makam" here, which is used in Turkey, because we mainly refer to the concepts of music traditions practiced nowadays in Turkey. 3 Yekta cites the following reference for this definition: Dechevrens, A. (1898). Etudes de science musicale. 1-11, Paris. 4 The terms like çeşni, güçlü, karar and seyir are explained in various sections of the text. In addition short descriptions are provided in the glossary section at the end. 11 Signell's book (Signell, 2008) presents most of the crucial parts of this theory in a compact form. 12 We use this term because we want to avoid the misleading dichotomy of Western and non-Western music. 13 Should be Uşşak flavor. Unfortunately the eager effort of the renowned master Murat Aydemir is overshadowed by occasional errors in the representation. 14 Should be Rast flavor with acem pitch. This error is possibly due to a translation error.

“…However, the tonic as understood in the context of Indian art music is considerably different, consequently requiring the development of new and context-specific algorithms for automatic tonic identification. Whilst we shall focus on Indian art music in this paper, it is worth mentioning that context-specific algorithms have also been proposed for other music traditions, for example the work by Bozkurt, Yarman, Karaosmanoglu and Akkoç (2009), andŞentürk, on tonic identification in the Makam music of turkey.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

Gulati

Bellur

Salamon

et al. 2014

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rāg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rāg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.