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Reseach Article

K-Nearest Neighbour and Earth Mover Distance for Raaga Recognition

by Prof. Prasad Reddy P.V.G.D, B. Tarakeswara Rao, Dr. K.R Sudha, Hari CH.V.M.K
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 33 - Number 5
Year of Publication: 2011
Authors: Prof. Prasad Reddy P.V.G.D, B. Tarakeswara Rao, Dr. K.R Sudha, Hari CH.V.M.K
10.5120/4017-5705

Prof. Prasad Reddy P.V.G.D, B. Tarakeswara Rao, Dr. K.R Sudha, Hari CH.V.M.K . K-Nearest Neighbour and Earth Mover Distance for Raaga Recognition. International Journal of Computer Applications. 33, 5 ( November 2011), 30-38. DOI=10.5120/4017-5705

@article{ 10.5120/4017-5705,
author = { Prof. Prasad Reddy P.V.G.D, B. Tarakeswara Rao, Dr. K.R Sudha, Hari CH.V.M.K },
title = { K-Nearest Neighbour and Earth Mover Distance for Raaga Recognition },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 33 },
number = { 5 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 30-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume33/number5/4017-5705/ },
doi = { 10.5120/4017-5705 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:19:22.123822+05:30
%A Prof. Prasad Reddy P.V.G.D
%A B. Tarakeswara Rao
%A Dr. K.R Sudha
%A Hari CH.V.M.K
%T K-Nearest Neighbour and Earth Mover Distance for Raaga Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 33
%N 5
%P 30-38
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As far as the Raaga Recognition process, most probably the significant and straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier. The classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance are not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for finding distance between neighbours using Cosine Distance (CD), Earth Movers Distance (EMD) and formulas are used to identify nearest neighbours, algorithm for classification in training and testing for identifying raagas. From the results it is concluded that Earth Movers Distance (EMD) is producing better results than Cosine Distance measure. Keywords--- Raaga, Cosine Distance( CD), Earth Movers Distance (EMD), K-NN

References
  1. Rajeswari Sridhar, Geetha T. V, “Swara Indentification for South Indian Classical Music”, ICIT '06 Proceedings of the 9th International Conference on Information Technology, IEEE Computer Society, ISBN:0-7695-2635-7.
  2. Youngmoo E. Kim, Brian Whitman” Singer Identification in Popular Music Recordings Using Voice Coding Features”, citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.
  3. Paul G., Corine G.M., Christophe C., Vincent F. “automatic classification of environmental noise events by hidden Markov models”, citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52
  4. Berger. “Some factors in the recognition of timbre“. J. Audio. Eng. Soc. 30, pp. 396-406.
  5. Clark, Milner. “Dependence of timbre on the tonal loudness produced by musical instruments“. J. Audio. Eng. Soc. 12, pp. 28-31.
  6. Eagleson, H. W., Eagleson, O. W. “Identification of musical instruments when heard directly and over a public-address system“. J. Acoust. Soc. Am. 19, pp. 338-342.
  7. Strong, Clark. “Perturbations of synthetic orchestral wind instrument tones“. J. Acoust. Soc. Am., Vol. 41, pp. 277-285.
  8. Bhatkande.V (1934), Hindusthani Sangeet Paddhati.Sangeet Karyalaya, 1934.
  9. Schroeder.M.R (1968), ” Period histogram and product spectrum: New methods for fundamental-frequency measurement”, Journal of the Acoustical Society of America, , vol. 43, no. 4, 1968.
  10. A. Prasad et al. “Gender Based Emotion Recognition System for Telugu Rural Dialects using Hidden Markov Models” Journal of Computing: An International Journal, Volume2, Number 6 June 2010 NY, USA, ISSN: 2151-9617
  11. S. Dixon: “Multiphonic Note Identification”: Proc. 19th Australasian Computer Science Conference: Jan-Feb 2003.
  12. W. Chai and B. Vercoe: “Folk Music Classification Using Hidden Markov Models”: Proc. Internation Conference on Artificial Intelligence: June 2001.
  13. Tarakeswara Rao B et. All “A Novel Process for Melakartha Raaga Recognitionusing Hidden Markov Models (HMM)”, International Journal of Research and Reviews in Computer Science (IJRRCS), Volume 2, Number 2,April 2011, ISSN: 2079-2557.
  14. A. Ghias, J. Logan, D. Chamberlin and B. C. Smith: “Query by Humming – Musical Information Retrieval in an Audio Database”: Proc. ACM Multimedia, pp. 231-236: 1995.
  15. H. Deshpande, U. Nam and R. Singh: “MUGEC: Automatic Music Genre Classification”: Technical Report, Stanford University: June 2001.
  16. A. J. Viterbi: “Error bounds for convolutional codes and an asymptotically optimal decoding algorithm”: IEEE Transactions on Information Theory, Volume IT-13, pp.260-269: April 1967.
  17. L. E. Baum: “An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes”: Inequalities, Volume 3, pp. 1-8: 1972.
  18. L. E. Baum and T. Petrie: “Statistical inference for probabilistic functions of finite state Markov chains”: Ann.Math.Stat., Volume 37, pp. 1554-1563: 1966.
  19. Gaurav Pandey, Chaitanya Mishra, and Paul Ipe “TANSEN: a system for automatic raga identification”
  20. Eagleson, H. W., Eagleson, O. W. “Identification of musical instruments when heard directly and over a public-address system“. J. Acoust. Soc. Am. 19, pp. 338-342.
  21. Shingo Kuroiwa, Satoru Tsuge, Masahiko Kita, and Fuji Ren,
  22. 2007, “Speaker Identification Method Using Earth Mover’s Distance for CCC Speaker Recognition Evaluation 2006”, Computational Linguistics and Chinese Language Processing Vol. 12, No. 3, September 2007, pp. 239-254.
Index Terms

Computer Science
Information Sciences

Keywords

Cosine Distance Earth Mover Distance (EMD) K-Nearest Neighbour