Bird species recognition combining acoustic and sequence modeling

Abstract

The goal of this work was to explore modeling techniques to improve bird species classification from audio samples. We first developed an unsupervised approach to obtain approximate note models from acoustic features. From these note models we created a bird species recognition system by leveraging a phone n-gram statistical model developed for speaker recognition applications. We found competitive performance from the note n-gram system compared to a Gaussian mixture model baseline using the same acoustic features. We found an important gain by doing score-level combination relative to the best individual system results. We verified that on most of the bird species under study there was a gain from system combination.

Keywords: Bird species recognition, phone n-gram modeling, Gaussian mixture model.