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DOI: 10.14569/IJACSA.2016.070530
PDF

An Efficient Audio Classification Approach Based on Support Vector Machines

Author 1: Lhoucine Bahatti
Author 2: Omar Bouattane
Author 3: My Elhoussine Echhibat
Author 4: Mohamed Hicham Zaggaf

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 5, 2016.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: In order to achieve an audio classification aimed to identify the composer, the use of adequate and relevant features is important to improve performance especially when the classification algorithm is based on support vector machines. As opposed to conventional approaches that often use timbral features based on a time-frequency representation of the musical signal using constant window, this paper deals with a new audio classification method which improves the features extraction according the Constant Q Transform (CQT) approach and includes original audio features related to the musical context in which the notes appear. The enhancement done by this work is also lay on the proposal of an optimal features selection procedure which combines filter and wrapper strategies. Experimental results show the accuracy and efficiency of the adopted approach in the binary classification as well as in the multi-class classification.

Keywords: Classification; features; selection; timbre; SVM; IRMFSP; RFE-SVM; CQT

Lhoucine Bahatti, Omar Bouattane, My Elhoussine Echhibat and Mohamed Hicham Zaggaf, “An Efficient Audio Classification Approach Based on Support Vector Machines” International Journal of Advanced Computer Science and Applications(IJACSA), 7(5), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070530

@article{Bahatti2016,
title = {An Efficient Audio Classification Approach Based on Support Vector Machines},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070530},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070530},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {5},
author = {Lhoucine Bahatti and Omar Bouattane and My Elhoussine Echhibat and Mohamed Hicham Zaggaf}
}



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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