Abstract
This paper deals with tree cutting real-world problem, causing significant damages to forests. The sensing and classification of acoustic signal emitted during tree cutting, is used to extract information of tree cutting events using sensors. Detecting the acoustic signal due to saw scratching power level in presence of ambiance noise and the other choral noise sources is a major issue in a forest environment. An acoustic sensor experimental setup is established for capturing the acoustic signal generated due to cross cut sawing with varying distances. Based on the experimental analysis, saw scratching acoustic signal is found with appropriate for tree cutting detection. The acoustic signal pre-processing is performed with the help of a SNR algorithm. The extraction of features in frequency space is done by using modified MFCC and spectral features extraction. Modified MFCC feature based dynamic time warping (MDTW) and spectral feature based Gauss-Bayesian classifier (SGBC) are used and compared.
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