Classification of Indian Classical Instruments Using Spectral and Principal Component Analysis Based Cepstrum Features

Abstract

In applications such as music information and database retrieval systems, classification of musical instruments plays an important role. The proposed work presents automatic classification of Indian Classical instruments based on spectral and MFCC features using well trained back propogation neural network classifier. Musical instruments such as Harmonium, Santoor and Tabla are considered for an experimentation. The spectral features such as amplitude and spectral range along with Mel Frequency Cepstrum Coefficients are considered as features. Being features are not distinguished, classification is done using non parametric classifiers such as neural networks. Being number of cepstrum coefficients are large important coefficients are selected using Principal Component Analysis. It has been observed that using 42 samples for training and 18 for testing, back propogation neural network provides accuracy of 98 % .The present work can be extended for more number of Hindustani and Carnitic classical musical Instruments.

Automatic genre classification of Indian Tamil and western music using fractional MFCC

“…It has further been observed, that very less work has been done on Indian music (Gaikwad et al 2014;Jothilakshmi and Kathiresan 2012;Kini et al 2011;Krishnaswamy 2003;Kumar et al 2014;Nagavi and Bhajantri 2011;Rao 2012), especially in South Indian Tamil music (Betsy and Bhalke 2015). Thus, the next sections of the paper presents a novel feature extraction scheme for automatic music genre classification of Indian Tamil music, that include FrFT based MFCC features or fractional MFCC using two classifiers namely KNN and SVM.…”