Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features

The increase in processing power and storage of computer has resulted in the growth of digital musical files, which demands some form of organization such as classification of the files. Typically, manual classification is used but it is expensive both in terms of time and money. One alternative...

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Main Author:	Mohd. Norowi, Noris
Format:	Thesis
Language:	English
Published:	2007
Subjects:	Automatic classification Folk music - Malaysia
Online Access:	http://psasir.upm.edu.my/id/eprint/5205/1/FSKTM_2007_7a.pdf
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id	my-upm-ir.5205
record_format	uketd_dc
spelling	my-upm-ir.52052015-05-22T01:55:04Z Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features 2007 Mohd. Norowi, Noris The increase in processing power and storage of computer has resulted in the growth of digital musical files, which demands some form of organization such as classification of the files. Typically, manual classification is used but it is expensive both in terms of time and money. One alternative solution is to automate musical genre classification. Existing systems have been developed to classify Western musical genres such as pop, rock and classical. However, adapting these systems for traditional Malay music is difficult due to the differences in musical structures and modes. In general, the musical structure of many genres in traditional Malay music is rhythmic and repetitive, which is different than Western music. This study investigates the effects of factors and audio feature set combinations towards the classification of traditional Malay musical genres. Ten traditional Malay musical genres are introduced in this study: Dikir Barat, Etnik Sabah, Gamelan, Ghazal, Inang, Joget, Keroncong, Tumbuk Kalang, Wayang Kulit and Zapin. The study is conducted in three phases. The first phase investigates the factors affecting classification of traditional Malay music: dataset size, track length, track location, number of cross-validation folds, and classifier. The second phase investigates the effect of feature set combinations on the classification result of traditional Malay music. The combinations are STFT, MFCC, STFT and Beat, MFCC and Beat, and STFT, MFCC and Beat. Following this, an automated classification system is developed and named MAGCLAST (Musical Analysis and Genre CLAssification System for Traditional Malay Music). The performance of MAGCLAST against three groups human (expert, trained and untrained) is tested in the final phase of the study. Results show that its classification at 66.3% is comparable to MARSYAS (61%) and trained human (70.6%). Interestingly, MAGCLAST also outperforms classification by average Malaysians, suggesting that an automated system for classifying traditional Malay music is certainly needed. Additionally, a small-scale study on human classification behaviour is also done to understand the factors that affect classification. It is hoped that the information could be exploited to enhance existing automated genre classification system in the near future. Automatic classification Folk music - Malaysia 2007 Thesis http://psasir.upm.edu.my/id/eprint/5205/ http://psasir.upm.edu.my/id/eprint/5205/1/FSKTM_2007_7a.pdf application/pdf en public masters Universiti Putra Malaysia Automatic classification Folk music - Malaysia Computer Science and Information Technology
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Automatic classification Folk music - Malaysia
spellingShingle	Automatic classification Folk music - Malaysia Mohd. Norowi, Noris Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features
description	The increase in processing power and storage of computer has resulted in the growth of digital musical files, which demands some form of organization such as classification of the files. Typically, manual classification is used but it is expensive both in terms of time and money. One alternative solution is to automate musical genre classification. Existing systems have been developed to classify Western musical genres such as pop, rock and classical. However, adapting these systems for traditional Malay music is difficult due to the differences in musical structures and modes. In general, the musical structure of many genres in traditional Malay music is rhythmic and repetitive, which is different than Western music. This study investigates the effects of factors and audio feature set combinations towards the classification of traditional Malay musical genres. Ten traditional Malay musical genres are introduced in this study: Dikir Barat, Etnik Sabah, Gamelan, Ghazal, Inang, Joget, Keroncong, Tumbuk Kalang, Wayang Kulit and Zapin. The study is conducted in three phases. The first phase investigates the factors affecting classification of traditional Malay music: dataset size, track length, track location, number of cross-validation folds, and classifier. The second phase investigates the effect of feature set combinations on the classification result of traditional Malay music. The combinations are STFT, MFCC, STFT and Beat, MFCC and Beat, and STFT, MFCC and Beat. Following this, an automated classification system is developed and named MAGCLAST (Musical Analysis and Genre CLAssification System for Traditional Malay Music). The performance of MAGCLAST against three groups human (expert, trained and untrained) is tested in the final phase of the study. Results show that its classification at 66.3% is comparable to MARSYAS (61%) and trained human (70.6%). Interestingly, MAGCLAST also outperforms classification by average Malaysians, suggesting that an automated system for classifying traditional Malay music is certainly needed. Additionally, a small-scale study on human classification behaviour is also done to understand the factors that affect classification. It is hoped that the information could be exploited to enhance existing automated genre classification system in the near future.
format	Thesis
qualification_level	Master's degree
author	Mohd. Norowi, Noris
author_facet	Mohd. Norowi, Noris
author_sort	Mohd. Norowi, Noris
title	Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features
title_short	Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features
title_full	Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features
title_fullStr	Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features
title_full_unstemmed	Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features
title_sort	improvement of automatic genre classification system for traditional malaysian music using beat features
granting_institution	Universiti Putra Malaysia
granting_department	Computer Science and Information Technology
publishDate	2007
url	http://psasir.upm.edu.my/id/eprint/5205/1/FSKTM_2007_7a.pdf
_version_	1747810373146247168

Improvement of Automatic Genre Classification System for Traditional Malaysian Music Using Beat Features

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