Pattern Classification of Indian Classical Ragas based on Object Oriented Concepts

Musical pattern recognition is a challenging area of research as music is a combination of audio and speech; a signal of a particular music cannot be expressed through a linear expression only. For this reason musical pattern identification by mathematical expressions is critical. In this paper a new method is being proposed for cataloguing different melodious audio stream into some specific featured classes based on object oriented modeling. The classes should have some unique features to be specified and characterized; depending upon those properties of a particular piece of music it can be classified into different classes and subclasses. The concept is developed considering the non-trivial categorization problem due to vastness of Indian Classical Music (ICM). It can be expected that the novel concept can reduce the complication to the objective analysis of the classification problem of ICM as well as the world music.

[1]  Craig Stuart Sapp,et al.  Efficient Pitch Detection Techniques for Interactive Music , 2001, ICMC.

[2]  Ilya Shmulevich,et al.  Graph-based smoothing of class data with applications in musical key finding , 1999, NSIP.

[3]  I. Shmulevich,et al.  THE USE OF RECURSIVE MEDIAN FILTERS FOR ESTABLISHING THE TONAL CONTEXT IN MUSIC , 1998 .

[4]  Chaitanya Mishra,et al.  TANSEN: A System for Automatic Raga Identification , 2003, IICAI.

[5]  M. Pearce,et al.  Sweet Anticipation : Music and the Psychology of Expectation , 2007 .

[6]  Emilia Gómez,et al.  Estimating The Tonality Of Polyphonic Audio Files: Cognitive Versus Machine Learning Modelling Strategies , 2004, ISMIR.

[7]  Ching-Hua Chuan,et al.  Polyphonic Audio Key Finding Using the Spiral Array CEG Algorithm , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[8]  Parag Chordia Automatic rag classification using spectrally derived tone profiles , 2004, ICMC.

[9]  Dirk-Jan Povel,et al.  Rhythm complexity measures for music pattern recognition , 1998, 1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175).

[10]  I. Shmulevich,et al.  Establishing the tonal context for musical pattern recognition , 1997, Proceedings of 1997 Workshop on Applications of Signal Processing to Audio and Acoustics.

[11]  Edward J. Coyle,et al.  Perceptual Issues in Music Pattern Recognition: Complexity of Rhythm and Key Finding , 2001, Comput. Humanit..

[12]  Xuejing Sun A pitch determination algorithm based on subharmonic-to-harmonic ratio , 2000, INTERSPEECH.

[13]  A. Kameoka,et al.  Consonance theory part I: consonance of dyads. , 1969, The Journal of the Acoustical Society of America.

[14]  Tor Sverre Lande,et al.  Object oriented music analysis , 1994, Comput. Humanit..

[15]  R. Shepard,et al.  Quantification of the hierarchy of tonal functions within a diatonic context. , 1979, Journal of experimental psychology. Human perception and performance.

[16]  Parag Chordia Automatic Raag Classification of Pitch-tracked Performances Using Pitch-class and Pitch-class Dyad Distributions , 2006, ICMC.

[17]  C. Krumhansl Cognitive Foundations of Musical Pitch , 1990 .

[18]  Matthew E. P. Davies,et al.  A Combined Phase and Amplitude Based Approach to Onset Detection for Audio Segmentation , 2003 .

[19]  I. Shmulevich,et al.  A system for machine recognition of music patterns , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[20]  R. Sengupta Study on some aspects of the “singer's formant” in north indian classical singing , 1990 .