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.

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},
}