Automatic Raaga Identification System For Carnatic Music Using Hidden Markov Model

B. Tarakeswara Rao; Dr. Prasad Reddy P.V.G.D; Dr. K.R Sudha

Automatic Raaga Identification System For Carnatic Music Using Hidden Markov Model

B. Tarakeswara Rao * §

Contact

Dr. Prasad Reddy P.V.G.D

Contact

Dr. K.R Sudha

Contact

Dr. CH.V.M.K.Hari

Contact

§ Vignan University

Automatic Raaga Identification System For Carnatic Music Using Hidden Markov Model

Article Fingerprint

ReserarchID

CST2Y31D

Automatic Raaga Identification System For Carnatic Music Using Hidden Markov Model Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

Abstract

As for as the Human Computer Interactions (HCI) is concerned, there is broad range of applications in the area of research in respective of Automatic Melakarta Raaga Identification in music. The pattern of identification is the main object for which, the basic mathematical tool is utilized. On verification, it is observed that no model is proved consistently and effectively to be predicted in its classification. This paper is, therefore, introduces a procedure for Raaga Identification with the help of Hidden Markov Models (HMM) which is rather an appropriate approach in identifying Melakarta Raagas. This proposed approach is based on the standard speech recognition technology by using Hidden continuous Markov Model. Data is collected from the existing data base for training and testing of the method with due design process relating to Melakarta Raagas. Similarly, to solve the problem of automatic identification of raagas, a suitable approach from the existing database is presented. The system, particularly, this model is based on a Hidden Markov Model enhanced with Pakad string matching algorithm. The entire system is built on top of an automatic note transcriptor. At the end, detailed elucidations of the experiments are given. It clearly indicates the effectiveness and applicability of this method with its intrinsic value and significance.

References

19 Cites in Article

Reference Format

V Bhatkande (1934). Hindusthani Sangeet Paddhati.Sangeet Karyalaya.
P Chordia (2006). Automatic raag classi_cation of pitch-tracked performances using pitch-class and pitch-class dyad distributions.
D Weenink Praat: doing phonetics by computer.
M Choudhary,P Ray Measuring Similarities Across Musical Composi-tions: An Approach Based on the Raga Paradigm.
L Rabiner (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.
C Iliopoulos,M Kurokawa (2002). String Matching with Gaps for Musical Melodic Recognition.
H Sahasrabuddhe (1994). Searching for a Common Language of Ragas.
R Upadhye,H Sahasrabuddhe (1992). On the Computational Model of Raag Music of India.
G Tzanetakis,G Essl,P Cook (2001). Automatic Musical Genre Classification of Audio Signals.
J Ghias,D Logan,B Chamberlin,Smith (1995). Query by Humming -Musical Information Retrieval in an Audio Database.
H Deshpande,U Nam,R Singh (2001). Stanford University.
S Dixon (2003). Proceedings. 19th Annual Computer Security Applications Conference.
W Chai,B Vercoe (2001). Folk Music Classification Using Hidden Markov Models.
J Viterbi (1967). Error bounds for convolutional codes and an asymptotically optimal decoding algorithm.
Leonard Baum,J Eagon (1972). An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology.
L Baum,T Petrie (1966). Statistical inference for probabilistic functions of finite state Markov chains.
Gaurav Pandey,Chaitanya Mishra,Paul Ipe,; Prasad (2010). Gender Based Emotion Recognition System for Telugu Rural Dialects using Hidden Markov Models.
Tarakeswara Rao,B (2011). All "A Novel Process for Melakartha Raaga Recognitionusing Hidden Markov Models (HMM).
L Arslan Speech toolbox in MAT LAB.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

B. Tarakeswara Rao, Dr. Prasad Reddy P.V.G.D, Dr. K.R Sudha, Dr. CH.V.M.K.Hari. 1970. "Automatic Raaga Identification System For Carnatic Music Using Hidden Markov Model". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 22).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 11 Issue 22

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Hidden Markov Models (HMM) classifier Melakarta Raaga Pakad Raaga Recognition

Classification

GJCST Classification G.3

I.2.6

Submission ReceivedOctober 29, 2011
Peer Review Double Blind
Handling Editor
Accepted November 22, 2011
Published December 4, 2011

Version of record

v1.2

Issue date

January 12, 2012

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 154

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. Prasad Reddy P.V.G.D ,B. Tarakeswara Rao , Dr. K.R Sudha,Hari CH.V.M.K. (PhD/Dr. count: 2)

View Count (all-time): 381

Total Views (Real + Logic): 4397

Total Downloads (simulated): 308

Publish Date: 2011 12, Sun

Monthly Totals (Real + Logic):

Month 1: 22 views
Month 2: 31 views
Month 3: 28 views
Month 4: 58 views
Month 5: 25 views
Month 6: 52 views
Month 7: 22 views
Month 8: 36 views
Month 9: 48 views
Month 10: 34 views
Month 11: 40 views
Month 12: 48 views
Month 13: 32 views
Month 14: 16 views
Month 15: 24 views
Month 16: 26 views
Month 17: 32 views
Month 18: 35 views
Month 19: 35 views
Month 20: 37 views
Month 21: 23 views
Month 22: 50 views
Month 23: 36 views
Month 24: 19 views
Month 25: 27 views
Month 26: 25 views
Month 27: 31 views
Month 28: 41 views
Month 29: 30 views
Month 30: 42 views
Month 31: 38 views
Month 32: 41 views
Month 33: 29 views
Month 34: 26 views
Month 35: 30 views
Month 36: 34 views
Month 37: 41 views
Month 38: 29 views
Month 39: 19 views
Month 40: 30 views
Month 41: 31 views
Month 42: 26 views
Month 43: 39 views
Month 44: 39 views
Month 45: 30 views
Month 46: 28 views
Month 47: 41 views
Month 48: 17 views
Month 49: 36 views
Month 50: 28 views
Month 51: 43 views
Month 52: 41 views
Month 53: 21 views
Month 54: 20 views
Month 55: 21 views
Month 56: 30 views
Month 57: 32 views
Month 58: 18 views
Month 59: 24 views
Month 60: 32 views
Month 61: 32 views
Month 62: 29 views
Month 63: 21 views
Month 64: 23 views
Month 65: 14 views
Month 66: 17 views
Month 67: 20 views
Month 68: 25 views
Month 69: 18 views
Month 70: 24 views
Month 71: 30 views
Month 72: 37 views
Month 73: 31 views
Month 74: 40 views
Month 75: 32 views
Month 76: 22 views
Month 77: 34 views
Month 78: 18 views
Month 79: 30 views
Month 80: 17 views
Month 81: 23 views
Month 82: 29 views
Month 83: 43 views
Month 84: 39 views
Month 85: 36 views
Month 86: 43 views
Month 87: 13 views
Month 88: 37 views
Month 89: 30 views
Month 90: 50 views
Month 91: 41 views
Month 92: 28 views
Month 93: 26 views
Month 94: 12 views
Month 95: 41 views
Month 96: 20 views
Month 97: 24 views
Month 98: 11 views
Month 99: 35 views
Month 100: 35 views
Month 101: 26 views
Month 102: 35 views
Month 103: 14 views
Month 104: 29 views
Month 105: 32 views
Month 106: 39 views
Month 107: 29 views
Month 108: 40 views
Month 109: 25 views
Month 110: 20 views
Month 111: 39 views
Month 112: 38 views
Month 113: 19 views
Month 114: 35 views
Month 115: 43 views
Month 116: 32 views
Month 117: 36 views
Month 118: 31 views
Month 119: 33 views
Month 120: 34 views
Month 121: 19 views
Month 122: 43 views
Month 123: 40 views
Month 124: 29 views
Month 125: 34 views
Month 126: 28 views
Month 127: 19 views
Month 128: 42 views
Month 129: 25 views
Month 130: 34 views
Month 131: 43 views
Month 132: 22 views
Month 133: 42 views
Month 134: 32 views
Month 135: 16 views
Month 136: 50 views
Month 137: 13 views
Month 138: 34 views
Month 139: 26 views
Month 140: 30 views
Month 141: 25 views
Month 142: 41 views
Month 143: 29 views
Month 144: 39 views
Month 145: 42 views
Month 146: 36 views
Month 147: 14 views
Month 148: 47 views
Month 149: 33 views
Month 150: 39 views
Month 151: 39 views
Month 152: 24 views
Month 153: 29 views
Month 154: 19 views
Month 155: 42 views
Month 156: 35 views
Month 157: 34 views
Month 158: 16 views
Month 159: 19 views
Month 160: 35 views
Month 161: 20 views
Month 162: 25 views
Month 163: 27 views
Month 164: 33 views
Month 165: 39 views
Month 166: 29 views
Month 167: 26 views
Month 168: 31 views
Month 169: 31 views
Month 170: 30 views
Month 171: 33 views
Month 172: 18 views
Month 173: 34 views
Month 174: 20 views
Month 175: 53 views
Month 176: 48 views
Month 177: 59 views
Month 178: 43 views

Total Views: 4.4K

Total Downloads: 308

All Trends