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Implementation of audio signal processing for automatic indonesian musical genre classification

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Musical genres can be defined as categorical description of music based on its behavior. Indonesia as a multicultural nation which has a wide diversity in the area of music does not have a categorical description for its local and traditional music. This study focuses on finding the pattern of Indonesian music with experimenting several genre-sets with the proposed feature-sets: musical surface features, MFCC, and rhythm features. Various experiments relating to feature extraction are conducted which resulted in utilizing all feature-sets producing the best classification performance. Separate experiments of local and traditional Indonesian genres are conducted, showing the classification performance of each genre-set, with traditional genre-set delivers the highest classification accuracy. Classification tasks are implemented in real-time and non real-time, using decision trees and Naive Bayes classifier. The results are evaluated and compared to validate the strength of each feature-set and to synchronize the human musical perception with the machine musical perception.

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Availability

B00524

(wh)

Available

Detail Information

Series Title

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Call Number

524

Publisher

: Swiss German University., 2009

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

IT

Specific Detail Info

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