Chirplet Transform Applied to Simulated and Real Blue Whale (Balaenoptera musculus) Calls

  • Mohammed Bahoura
  • Yvan Simard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

Chirplet transform performance to identify low-frequency blue whale calls is tested with simulations and observations from North-West Atlantic. The three different calls are simulated using linear or quadratic frequency sweeping chirps and a hanning window. The performance of Chirplet transform to accurately estimate the chirp parameters with or without noise is first assessed. Then the performance to classify the real vocalizations from the test dataset using the three features best estimated from the simulations is then assessed. The method has a high classification rate and appears promising to efficiently identify these blue whale signature vocalizations with a reduced number of parameters, even under low signal to noise ratios.

Keywords

Blue whale vocalizations chirplet transform feature extraction vector quantization noise 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mohammed Bahoura
    • 1
  • Yvan Simard
    • 2
  1. 1.Département de Mathématiques, d’Informatique et de GénieUniversité du Québec à RimouskiRimouskiCanada
  2. 2.Institut des Sciences de la MerUniversité du Québec à RimouskiRimouskiCanada

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