Neural network based Indian Folk Dance song classification using MFCC and LPC

Abstract

A large number of folk dance videos are uploaded on the web or added as a situational song in the Bollywood movies. The classification of folk dance videos is essential for dance education, to preserve cultural heritage, and for music companies to provide better customer oriented service. India is a country having many regional languages and each region has its own popular folk dances. Four different Indian folk dances namely, 'Garba', 'Lavani', 'Ghoomar' and 'Bhangra' are considered. A Folk Dance Classification Framework is proposed which extracts audio signal from video, takes a fragment of 125 seconds from the beginning and further separates it into a set of small segments, calculates Mel-frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients, generates high dimensional feature vector, reduces dimensionality using Principle Component Analysis (PCA) and classifies segments using Scale Conjugate Gradient Neural Network. The performances of chosen classifiers, K-Nearest Neighbor, Naïve Bayes and Neural Networks, are compared. Class labels of all segments are clubbed together and based on majority voting class label is assigned to a folk dance song. System achieves more than 90% accuracy.