A robust automatic bird phrase classifier using dynamic time-warping with prominent region identification

摘要

In this paper, we present a novel approach to birdsong phase classification using template-based techniques suitable even for limited training data and noisy environments. The algorithm utilizes dynamic time-warping and prominent (high-energy) time-frequency regions of training spectrograms to derive templates. The algorithm is evaluated on 32 classes of Cassin's Vireo bird phrases. Using only three training examples per class, our algorithm yields a phrase accuracy of 96.23%, outperforming other classifiers (e.g. 85.21% classification accuracy of SVM). In the presence of additive noise (10 dB SNR degradation), the proposed classifier does not degrade significantly, compared to others.