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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/5205/1/FSKTM_2007_7a.pdf |
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| Summary: | 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. |
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