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Beaked whale are difficult to observe visually and are among the most sensitive species to anthropogenic noise. Thus passive acoustic monitoring is particularly interesting to study these species and mitigate noise impact. This paper presents the outline of an automatic recognition method of beaked whale signals. This method has three steps: a transient detector, individual click classification and click association in click trains using a multi- hypothesis tracker. Click train association enhances classification because a click is not emitted alone and click trains have properties that can be characteristic of a species (e.g. inter-click-interval). The results are presented for three species: Blainville’s, Cuvier’s and Gervais beaked whale. The results obtained are very encouraging.
detection, classification, beaked-whale, target tracking, data association.
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