Bird-phrase segmentation and verification: A noise-robust template-based approach

In this paper, we present a birdsong-phrase segmentation and verification algorithm that is robust to limited training data, class variability, and noise. The algorithm comprises a noise-robust, Dynamic-Time-Warping (DTW)-based segmentation and a discriminative classifier for outlier rejection. The algorithm utilizes DTW and prominent (high energy) time-frequency regions of training spectrograms to derive a reliable noise-robust template for each phrase class. The resulting template is then used for segmenting continuous recordings to obtain segment candidates whose spectrogram amplitudes in the prominent regions are used as features to a Support Vector Machine (SVM). The algorithm is evaluated on the Cassin´s Vireo recordings; our proposed system yields low Equal Error Rates (EER) and segment boundaries that are close to those obtained from manual annotations and, is better than energy or entropy-based birdsong segmentation algorithms. In the presence of additive noise (-10 to 10 dB SNR), the proposed phrase detection system does not degrade as significantly as the other algorithms do.