Recognition of Melakartha Raagas with the Help of Gaussian Mixture Model

Main Article Content

Tarakeswara Rao B
Dr. Prasad Reddy P.V.G.D, Prasad A


Recognizing Melakartha raagas from speech has gained immense attention recently. With the increasing demand for human computer
interaction, it is necessary to understand the state of the singer. In this paper an attempt is made to recognize and classify the raagas from the
singers database where the classification is mainly based on extracting several key features like Mel Frequency Cepstral Coefficients (MFCCs)
from the speech signals of those persons by using the process of feature extraction. For training and testing of the method, data is collected from
the existing database with due verification relating to melakartha raagas. The 72 melakartha raagas for training, of them, a few raagas were
specifically selected and tested. Then it is found that all the tested raagas are well recognized. In another case the 52 melakartha raagas for
training and another 20 raagas for testing. The experiments were performed pertaining to singer raagas. Using a statistical model like Gaussian
Mixture Model classifier (GMM) and features extracted from these speech signals, we build a unique identity for each raaga that enrolled for
raaga recognition. Expectation and Maximization (EM) algorithm, an elegant and powerful method is used with latent variables for finding the
maximum likelihood solution, to test the other raagas against the database of all singers who enrolled in the database.


Keywords: Raaga Recognition, Gaussian Mixture Model (GMM) classifier, Sequential Forward Selection, EM algorithm, Mel Frequency
Cepstral Coefficients(MFCCs).


Download data is not yet available.

Article Details