Recognition of Melakartha Raagas with the Help of Gaussian Mixture Model
Main Article Content
Abstract
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.
Â
Keywords: Raaga Recognition, Gaussian Mixture Model (GMM) classifier, Sequential Forward Selection, EM algorithm, Mel Frequency
Cepstral Coefficients(MFCCs).
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.