Realization of an Intelligent Frog Call Identification Agent

Abstract

An intelligent frog call identification agent is developed in this work to provide the public to easily consult online. The raw frog call samples are first filtered by noise removal, high frequency compensation and discrete wavelet transform techniques in order. An adaptive end-point detection segmentation algorithm is proposed to effectively separate the individual syllables from the noise. Four features are extracted and serve as the input parameters of the classifier. Three well-known classifiers, the k-th nearest neighboring, Support Vector Machines and Gaussian Mixture Model, are employed in this work for comparison. A series of experiments were conducted to measure the outcome performance of the proposed agent. Experimental results exhibit that the recognition rate for Gaussian Mixture Model algorithm can achieve up to the best performance. The effectiveness of the proposed frog call identification agent is thus verified.