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DOI: 10.14569/IJACSA.2019.0101270
PDF

KNN and SVM Classification for Chainsaw Sound Identification in the Forest Areas

Author 1: N’tcho Assoukpou Jean GNAMELE
Author 2: Yelakan Berenger OUATTARA
Author 3: Toka Arsene KOBEA
Author 4: Geneviève BAUDOIN
Author 5: Jean-Marc LAHEURTE

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

  • Abstract and Keywords
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Abstract: We present in this paper a comparative study of two classifiers, namely, SVM (support vector machine) and KNN (K-Nearest Neighbors), which we combine to MFCC (Mel-Frequency Cepstral Coefficients) in order to make possible the detection of chainsaw’s sounds in a forest environment. Optimization’s calculation of the relevant characteristics of the sounds recorded in the forest and the judicious choice of the key parameters of the classifiers allows us to obtain a true positive rate of 95.63% for the SVM-LOG-KERNEL and 94.02% for the KNN. The SVM-LOG-KERNEL classifier offers a better classification result and a processing time 30 times faster than KNN.

Keywords: KNN Algorithm; SVM Algorithm; MFCC; sound recognition; forest monitoring; machine learning

N’tcho Assoukpou Jean GNAMELE, Yelakan Berenger OUATTARA, Toka Arsene KOBEA, Geneviève BAUDOIN and Jean-Marc LAHEURTE, “KNN and SVM Classification for Chainsaw Sound Identification in the Forest Areas” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101270

@article{GNAMELE2019,
title = {KNN and SVM Classification for Chainsaw Sound Identification in the Forest Areas},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101270},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101270},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {N’tcho Assoukpou Jean GNAMELE and Yelakan Berenger OUATTARA and Toka Arsene KOBEA and Geneviève BAUDOIN and Jean-Marc LAHEURTE}
}



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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