Traditional Malay musical genre classification: machine versus human / Noris Mohd Norowi ... [et al.]

As the number of digital musical files grows, there is a demand for a form of organization of these rapidly growing collections. One such instance is through classifying and sorting the musical files into their respective genres. However, manual classification is expensive both in terms of time and...

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Main Authors: Mohd Norowi, Noris (Author), Madzin, Hizmawati (Author), C. Doraisamy, Shyamala (Author), O.K. Rahmat, Rahmita Wirza (Author)
Format: Book
Published: 2015-12.
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Summary:As the number of digital musical files grows, there is a demand for a form of organization of these rapidly growing collections. One such instance is through classifying and sorting the musical files into their respective genres. However, manual classification is expensive both in terms of time and money. A solution to this is to automate this step, a process which is referred to as automatic 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 presents the factors which affect the automatic genre classification of traditional Malay musical genres on the ten traditional Malay musical genres included which are Dikir Barat, Etnik Sabah, Gamelan, Ghazal, Inang, Joget, Keroncong, Tumbuk Kalang, Wayang Kulit and Zapin. 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 of 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. It is hoped that the information could be exploited to enhance existing automated genre classification system in the near future.
Item Description:https://ir.uitm.edu.my/id/eprint/35720/2/35720.pdf