Classification of underwater mammals using feature extraction based on time-frequency analysis and BCM theory

Underwater mammal sound classification is demonstrated using a novel application of wavelet time-frequency decomposition and feature extraction using a Bienenstock, Cooper, and Munro (BCM) unsupervised network. Different feature extraction methods and different wavelet representations are studied. The system achieves outstanding classification performance even when tested with mammal sounds recorded at very different locations (from those used for training). The improved results suggest that nonlinear feature extraction from wavelet representations outperforms different linear choices of basis functions.