Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/8230
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dc.contributor.authorUpadhyaya, P.
dc.contributor.authorSuma, S.M.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2020-03-30T10:18:14Z-
dc.date.available2020-03-30T10:18:14Z-
dc.date.issued2015
dc.identifier.citation2015 8th International Conference on Contemporary Computing, IC3 2015, 2015, Vol., , pp.127-131en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8230-
dc.description.abstractIn this work, an effort has been made to differentiate the allied raagas in Carnatic music. Allied raagas are the raagas that are composed using same set of notes. The features derived from the pitch sequence are used for differentiating these raagas. The coefficients of legendre polynomials, used to fit the pitch contours of the song clips are used for identifying raagas. Obtained features are validated using different classifiers such as Neural networks, Naive Bayes, Multi class classifier, Bagging and Random forest. The proposed system is tested on 4 sets of allied raagas. Naive Bayes classifier gives an average accuracy of 86.67% for allied set of Todi-Dhanyasi and Multi class classifier gives an average accuracy of 86.67% for allied set of Kharaharapriya-Anandabhairavi-Reethigoula. In general, Neural network classifier performance is found to be better than other classifiers. � 2015 IEEE.en_US
dc.titleIdentification of allied raagas in Carnatic musicen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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