Paper Info
Title
The ideal data representation for feature extraction of traditional Malay musical instrument sounds classification
Abstract
In presenting the appropriate data sets, various data representation and feature extraction methods have been discovered previously. However, almost all the existing methods are utilized based on the Western musical instruments. In this study, the data representation and feature extraction methods are applied towards Traditional Malay musical instruments sounds classification. The impact of five factors that might affecting the classification accuracy which are the audio length, segmented frame size, starting point, data distribution and data fraction (for training and testing) are investigated. The perception-based and MFCC features schemes with total of 37 features was used. While, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets in terms of the classification performance. The results show that the highest accuracy of 97.37% was obtained from the best data sets with the combination of full features.
Year
Venue
Keywords
2010
ICIC (1)
ideal data representation,classification accuracy,traditional malay musical instrument,appropriate data set,feature extraction method,modified data set,data fraction,data distribution,data representation,best data set,classification performance,various data representation,multi layer perceptron,feature extraction
Field
DocType
Volume
Mel-frequency cepstrum,Data set,External Data Representation,Pattern recognition,Malay,Computer science,Musical instrument,Feature extraction,Artificial intelligence,Classifier (linguistics),Perceptron,Machine learning
Conference
6215
ISSN
ISBN
Citations 
0302-9743
3-642-14921-9
2
PageRank 
References 
Authors
0.38
8
5
Name
Order
Citations
PageRank
Norhalina Senan1114.01
Rosziati Ibrahim24613.87
Nazri Mohd Nawi315822.90
Musa Mohd Mokji4387.00
Tutut Herawan560875.21