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

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.

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