Recognition of Melakartha Raagas with the Help of Gaussian Mixture Model

Recognizing Melakartha raagas from speech has gained immense attention recently. With the increasing demand for human computerinteraction, it is necessary to understand the state of the singer. In this paper an attempt is made to recognize and classify the raagas from thesingers 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 fromthe existing database with due verification relating to melakartha raagas. The 72 melakartha raagas for training, of them, a few raagas werespecifically selected and tested. Then it is found that all the tested raagas are well recognized. In another case the 52 melakartha raagas fortraining and another 20 raagas for testing. The experiments were performed pertaining to singer raagas. Using a statistical model like GaussianMixture Model classifier (GMM) and features extracted from these speech signals, we build a unique identity for each raaga that enrolled forraaga recognition. Expectation and Maximization (EM) algorithm, an elegant and powerful method is used with latent variables for finding themaximum likelihood solution, to test the other raagas against the database of all singers who enrolled in the database