Abstract

    Characterization of marine mammal vocalizations is of great help for understanding underwater issues or for species monitoring. The vocalizations of the North-East Pacific (NEPAC) blue whales are known to be made of at least three different call types. This study aims at the development of a wholly automatic process of detection and classification for the two most common call types of the NEPAC population. Using time-frequency tools and analyses to create features, we show that a simple Gaussian Mixture Model classifier can be used to accurately track and identify the call types in long vocalizations.