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Title: Raga classification for Carnatic music
Authors: Suma, S.M.
Koolagudi, S.G.
Issue Date: 2015
Citation: Advances in Intelligent Systems and Computing, 2015, Vol.339, , pp.865-875
Abstract: In this work, an effort has been made to identify raga of given piece of Carnatic music. In the proposed method, direct raga classification without the use of note sequence has been performed using pitch as the primary feature. The primitive features that are extracted from the probability density function (pdf) of the pitch contour are used for classification. A feature vector of 36 dimension is obtained by extracting some parameters from the pdf. Since non-sequential features are extracted from the signal, artificial neural network (ANN) is used as a classifier. The database used for validating the system consists of 162 songs from 12 ragas. The average classification accuracy is found to be 89.5%. � Springer India 2015.
Appears in Collections:2. Conference Papers

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