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DOI: 10.14569/IJACSA.2017.080844
PDF

Automatic Music Genres Classification using Machine Learning

Author 1: Muhammad Asim Ali
Author 2: Zain Ahmed Siddiqui

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 8, 2017.

  • Abstract and Keywords
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Abstract: Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also presents comparative analysis between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with dimensionality return and then without dimensionality reduction via principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to extract information for the data set. In addition, the MFCC features are used for individual tracks. From results we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results compare to the results with dimensionality reduction. Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%.

Keywords: K-nearest neighbor (k-NN); Support Vector Machine (SVM); music; genre; classification; features; Mel Frequency Cepstral Coefficients (MFCC); principal component analysis (PCA)

Muhammad Asim Ali and Zain Ahmed Siddiqui, “Automatic Music Genres Classification using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080844

@article{Ali2017,
title = {Automatic Music Genres Classification using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080844},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080844},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Muhammad Asim Ali and Zain Ahmed Siddiqui}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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