A Study On Feature Analysis Using Sparse Representation For Music Classification

May Thu Myint; Phyu Phyu Khaing

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A Study On Feature Analysis Using Sparse Representation For Music Classification

http://hdl.handle.net/20.500.12678/0000007419

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Publication type
		Journal article
Upload type
		Publication
Title
	Title	A Study On Feature Analysis Using Sparse Representation For Music Classification
	Language	en
Publication date		2019-12-12
Authors
		May Thu Myint
		Phyu Phyu Khaing
Description
		This paper presents the first attempt to classify for Myanmar ethnic music by using sparse representation classification method to define the class label according to their ethnic traditional style. In this system, the only five myanmar ethnic groups are considered such as Kachin, Kayin, Mon, Shan, Rakhine. The classification system describe the better accuracy by analysing the temporal features and spectral features, the best outcome by calculating all the results based on Sparse Representation classifier in compared with K Nearest Neighbors classifier. Therefore, this audio classification achieved the results by evaluating feature combination and the best feature combination by using SRC and KNN classfier. With all the features of all ethnic classes, the overall outcome of the SRC 64%, which is better than 54% of the overall KNN accuracy. All features (114) combination give the best results of 82% for Kayin ethnic songs than other ethnic songs.The feature combination of MFCC(std, deltamean) are tested on all of five ethnic classes which is the best classification results of 75.00% accuracy from SRC classifier than the classification results of 51.33% from KNN classifier.
Keywords
		myanmar ethnic music, sparse representation classifier, k nearest neighbors, temporal feature, spectral feature
Journal articles
		No-1
		Journal of Research and Applications (JRA), UCSMTLA, 2019
		80-87
		vol-1

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