Acoustic Classification of Bird Species Using Wavelets and Learning Algorithms

Abstract

In this project, we derived an effective and efficient mathematical algorithm to identify bird species based on bird calls. Classifying bird species can be useful in real applications, such as determining the health of an ecosystem, or identifying hazardous species of birds near airports and reducing the bird-aircraft strikes. Having well-trained ornithologists to identify the characteristics of birds requires many man hours, and the results may be subjective. Our research was intended to develop a semi-automatic classification algorithm. We first performed a wavelet decomposition algorithm over more than 1200 syllables from 12 different bird species, and then extracted a set of eight parameters from each instance. The dataset formed by the instances and associated parameters was used to train and test different classifiers. Our results showed that among all the classifiers we tested, Cubic Support Vector Machine and Random Forest achieved the highest classification rates, each of which was over 93%.