APPLICATIONS OF DATA MINING TECHNIQUES TO AUTOMATIC FROG IDENTIFICATION

Abstract

An intelligent frog call identifier is developed in this work to provide the public with easy online consultation. The raw frog call samples are first filtered by noise removal, high frequency compensation, and discrete wavelet transform techniques in that order. An adaptive end-point detection segmentation algorithm is proposed to effectively separate the individual syllables from the noise. Eight features, including spectral centroid, signal bandwidth, spectral roll-off, threshold-crossing rate, delta spectrum magnitude, spectral flatness, average energy, and mel-frequency cepstral coefficients are extracted and serve as the input parameters of the classifier. Three well-known classifiers, the kth nearest neighboring, a backpropagation neural network, and a naive Bayes classifier, are employed in this work for comparison. A series of experiments were conducted to measure the outcome performance of the proposed work. Experimental results show that the recognition rate of the k-nearest neighbor classifier with the parameters of mel-frequency cepstral coefficients can achieve up to 93.81%. The effectiveness of the proposed frog call identifier is thus verified.