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An Analysis of Persian Music and the Santur Instrument

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An Analysis of Persian Music and the Santur Instrument Automatic recognition of Persian musical modes in audio musical signals PhD Thesis Peyman Heydarian Music Technology Group Faculty of Art, Architecture and Design London Metropolitan University Email: [email protected] June 2016 Abstract This research proposes new approaches for computational identification of Persian musical modes. This involves constructing a database of audio musical files and developing computer algorithms to perform a musical analysis of the samples. Essential features, the spectral average, chroma, and pitch histograms, and the use of symbolic data, are discussed and compared. A tonic detection algorithm is developed to align the feature vectors and to make the mode recognition methods independent of changes in tonality. Subsequently, a geometric distance measure, such as the Manhattan distance, which is preferred, and cross correlation, or a machine learning method (the Gaussian Mixture Models), is used to gauge similarity between a signal and a set of templates that are constructed in the training phase, in which data-driven patterns are made for each dastgàh (Persian mode). The effects of the following parameters are considered and assessed: the amount of training data; the parts of the frequency range to be used for training; down sampling; tone resolution (12-TET, 24-TET, 48-TET and 53-TET); the effect of using overlapping or non- overlapping frames; and silence and high-energy suppression in pre-processing. The santur (hammered string instrument), which is extensively used in the musical database samples, is described and its physical properties are characterised; the pitch and harmonic deviations characteristic of it are measured; and the inharmonicity factor of the instrument is calculated for the first time. The results are applicable to Persian music and to other closely related musical traditions of the Mediterranean and the Near East. This approach enables content-based analyses of, and content- based searches of, musical archives. Potential applications of this research include: music information retrieval, audio snippet (thumbnailing), music archiving and access to archival content, audio compression and coding, associating of images with audio content, music transcription, music synthesis, music editors, music instruction, automatic music accompaniment, and setting new standards and symbols for musical notation. 2 Acknowledgements I am grateful for invaluable help and support from my family, friends, musicians, ethnomusicologists and scientists, and from the many others who have assisted me and collaborated with me during the course of my thesis. I would particularly like to thank my supervisors, Lewis Jones and Allan Seago, for their support and helpful advice during the course of my PhD research. I started this PhD in part because while I was lecturing on music technology at London Metropolitan University Lewis Jones suggested to me to pursue my PhD studies there, and this research has benefitted from his wide-ranging multidisciplinary approach to musical inquiry. I would like to thank my former BSc and MSc lecturers and supervisors in Shiraz and Tarbiat Modarres Universities: Professor Mohammad Ali Masnadi Shirazi for his support whenever I needed it, Professor Rahim Ghayour, Dr Mojtaba Lotfizad, and especially Professor Ehsanollah Kabir, who encouraged me to do my Master’s thesis on music note recognition for santur and directed me to this field. I would also like to thank Dr Kambiz Badie, my supervisor at Iran Telecom Research Center who for the first time encouraged me to explore the possibility of Persian dastgàh recognition. Thanks to Dr Christ Harte, Professor Juan Pablo Bello, Professor Baris Bozkurt and Professor Emilia Gomez for kindly sharing their computer codes with me. Thanks also to my MPhil supervisors Professor Josh Reiss and Professor Mark Plumbley. And thank you to my musician colleagues whose performances with me are used in my analysis and also for fruitful chats with them: Vassilis Chatzimakris, Avan Abdullah, Suna Alan, Aysegul Erdogan, Vasiliki Anastasiou, Cigdem Aslan, Dr Pejman Azarmina, Dr Mohammadali Merati, Olcay Bayir, Leonardo Cini, Aygul Erce, Parastoo Heydarian, Francesco Iannuzzelli, Mansour Izadpanah, Mohammad Jaberi, Ali Torshizi, Sarwat Koyi, Ewan Macdonald, Christelle Madani, Emad Rajabalipour, Rana Shieh, Forough Jokar, Shirley Smart, Rihab Azar and Elaha Soroor. Thanks to my mother and sister, Nasrin Danesh and Parastoo Heydarian, for their love and support. This thesis is dedicated to my family and to the memory of my father Heshmatollah Heydarian. Finally, thanks to Ed Emery, Afsaneh Rasaei, Nasser Danesh, Seddigh Tarif, and to Professor Owen Wright for the invaluable discussions and musical collaborations that we have had. 3 Table of Contents 1 Introduction .......................................................................................................................... 11 1.1 Motivations and the aims of this PhD research ........................................................... 11 1.2 Applications of automated dastgàh recognition ......................................................... 11 1.3 Persian musical scales and their structure ................................................................... 12 1.4 Research challenges presented by Persian scales ....................................................... 12 1.5 Context, scope and constraints of the research ........................................................... 13 1.6 Contributions of the thesis .......................................................................................... 14 1.7 Outline of the thesis .................................................................................................... 17 2 Background to Persian music and the santur ....................................................................... 18 2.1 Persian or Iranian ........................................................................................................ 18 2.2 Persian intervals .......................................................................................................... 18 2.3 Persian modes ............................................................................................................. 22 2.4 Composition ................................................................................................................ 23 2.5 Social and cultural context of music ........................................................................... 26 2.6 The santur and its physical properties ......................................................................... 27 2.7 The fundamental frequencies and their overtones ...................................................... 30 3 Literature review: the application of musical signal processing techniques to Western and non-Western musics ............................................................................................ 34 3.1.1 Digital Signal Processing (DSP) ......................................................................... 34 3.1.2 Analogue to digital converters ............................................................................ 34 3.1.3 Music Information Retrieval (MIR) .................................................................... 34 3.2 Chord, key and melody recognition for Western tonal music ..................................... 36 3.2.1 Chord identification ............................................................................................ 36 3.2.2 Chord segmentation and recognition ................................................................... 36 3.2.3 Key recognition ................................................................................................... 36 3.2.4 A melodic similarity measure .............................................................................. 39 3.3 Mode recognition in World music................................................................................ 40 4 3.3.1 Work on Indian music: raag recognition ............................................................. 40 3.3.2 Work on Turkish music: tonic detection and makam identification .................... 41 3.3.3 Work on Arabic music: maqàm recognition, tonic detection .............................. 44 3.3.4 Work on Greek music: a pitch tracker with quartertone resolution ..................... 45 3.4 Works on Iranian music: pitch tracking and mode recognition .................................. 46 3.4.1 State of the art techniques for Persian pitch and mode recognition .................... 47 3.5 Commercially available audio search engines ............................................................ 49 4 The database ........................................................................................................................ 50 4.1 Introduction ................................................................................................................. 50 4.2 Structure of the database ............................................................................................. 50 4.2.1 Santur solo recordings made in a studio (db1 and its extensions) ....................... 50 4.2.2 Small ensemble recoded in public performance (db2) ........................................ 51 4.2.3 Old Greek gramophone recordings (db3) ............................................................ 51 4.2.4 Kamàncheh solo recordings made in a studio (db4) ........................................... 51 4.2.5 Piano solo recordings made in a studio (db5) ....................................................
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