German vs. Austrian folk song classification

Author

Khoo, Suisin ; Zhihong Man ; Zhenwei Cao ; Jinchuan Zheng

Author_Institution

Fac. of Eng. & Ind. Sci., Swinburne Univ. of Technol., Melbourne, VIC, Australia

fYear

2013

fDate

19-21 June 2013

Firstpage

131

Lastpage

136

Abstract

Computerized analysis and classification of folk songs receives increased attention in recent years. In this paper, we demonstrate a two-case German and Austrian folk song classification using the musical feature density map (MFDMap) as the musical features representation and the finite impulse response extreme learning machine (FIR-ELM) as the machine classifier. Fifteen different MFDMaps are designed to study the music properties that aid in characterizing the differences. Our simulations show that the FIR-ELM classifier can achieve 83% classification accuracy using the MFDMap with interval, duration and duration ratio features.

Keywords

FIR filters; learning (artificial intelligence); music; pattern classification; signal classification; Austrian folk song classification; FIR-ELM classifier; German folk song classification; MFDMap; classification accuracy; computerized folk song analysis; computerized folk song classification; duration ratio feature; finite impulse response extreme learning machine; interval feature; machine classifier; music properties; musical feature density map; musical feature representation; Folk song classification; artificial neural network; finite impulse response extreme learning machine; musical feature density map;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2013 8th IEEE Conference on

Conference_Location

Melbourne, VIC

Print_ISBN

978-1-4673-6320-4

Type

conf

DOI

10.1109/ICIEA.2013.6566353

Filename

6566353