Frog classification using machine learning techniques
An automatic frog sound identification system is developed in this work to provide the public to easily consult online. The sound samples are first properly segmented into syllables. Then three features, spectral centroid, signal bandwidth and threshold-crossing rate, are extracted to serve as the parameters for the frog sound classification. Two well-known classifiers, kNN and SVM, are adopted to recognize the frog species based on the three extracted features. The experimental results show that the average classification accuracy rate can be up to 89.05% and 90.30% for kNN and SVM classifiers, respectively. The effectiveness of the proposed on-line recognition system is thus verified. (C) 2008 Published by Elsevier Ltd.
classification segmentation feature extraction support vector machines kth nearest neighboring