Automatic han chinese folk song classification using extreme learning machines

Authors:
Suisin Khoo;Zhihong Man;Zhenwei Cao
Affiliations:
Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, VIC, Australia;Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, VIC, Australia;Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, VIC, Australia
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

Multilayer feedforward neural networks trained via supervised learning have proven to be successful in pattern recognition. This paper presents the technique of using single hidden layer feedforward neural network as an automatic classifier in music classification. Han Chinese folk songs from five distinct geographical regions in China are studied and encoded using a novel musical feature density map (MFDMap) for machine classification. The extreme learning machine (ELM) and its two variants are employed as the classifiers to categorize the folk songs. Our simulations show that by using a low-pass finite impulse response extreme learning machine (FIR-ELM), we can achieve 80.65% classification accuracy.