Music Genre Classification using Naïve Bayes Algorithm

Music Genre Classification using Naïve Bayes Algorithm

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2018 by IJCTT Journal
Volume-62 Number-1
Year of Publication : 2018
Authors : Ardiansyah, Boy Yuliadi, Riad Sahara
DOI :  10.14445/22312803/IJCTT-V62P107

MLA

MLA Style: Ardiansyah, Boy Yuliadi, Riad Sahara "Music Genre Classification using Naïve Bayes Algorithm" International Journal of Computer Trends and Technology 62.1 (2018): 50-57.

APA Style:Ardiansyah, Boy Yuliadi, Riad Sahara (2018). Music Genre Classification using Naïve Bayes Algorithm. International Journal of Computer Trends and Technology, 62(1), 50-57.

Abstract
Classification of music genres that are processed consist of, rock, pop, dangdut and jazz. The number of each genre for training data is 20 data for rock music, 20 data for pop music, 20 data for jazz music, and 17 data for dangdut music. While for data testing there are 10 data for rock music, 8 data for pop music, 10 data for jazz music, and 8 data for dangdut music. So that the total digital music that will be used is 113 digital music. Data taken in the form of * .mp3 data is changed into * .wav data. The classification method used is Naive Bayes. And feature features that are used are short time energy, zero crossing rate, spectral centroid and spectral flux. The results obtained from the overall testing data are correctly classified as 15 data from 36 data so that the success percentage is 41.67%. To classify the highest success is the highest classification of pop music by 70.5%, followed by dangdut music by 62.5%, then rock music by 30%. For jazz music the success of the classification is very small, which is only 10%.

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Keywords
Music genre, Classification, Naïve Bayes.