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Dorian Cazau

Researcher at Centre national de la recherche scientifique

Publications -  30
Citations -  350

Dorian Cazau is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Humpback whale & Whale. The author has an hindex of 7, co-authored 29 publications receiving 259 citations. Previous affiliations of Dorian Cazau include Pierre-and-Marie-Curie University & European University of Brittany.

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Animal-borne telemetry: An integral component of the ocean observing toolkit

Robert Harcourt, +60 more
TL;DR: The use of animal telemetry is a powerful tool for observing marine animals and the physical environments that they inhabit, from coastal and continental shelf ecosystems to polar seas and open oceans.
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New acoustic model for humpback whale sound production

TL;DR: In this article, the authors proposed a model to predict the sound unit durations and frequency formants of humpback whales using measurements of the trachea, laryngeal sac, and nasal cavities.
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Understanding the intentional acoustic behavior of humpback whales: a production-based approach.

TL;DR: A functional scenario of this respiratory tractus of humpback whales for the generation of vocal sounds is described, and an acoustic characterization of each of these configurations is proposed to link different spectral features, namely, fundamental frequencies and formant structures, to specific vocal production mechanisms.
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A study of vocal nonlinearities in humpback whale songs: from production mechanisms to acoustic analysis.

TL;DR: The results show that vocal nonlinearities may be a communication strategy that conveys information about the whale’s body size and physical fitness, and thus may be an important component of humpback whale songs.
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Wind Speed Estimation Using Acoustic Underwater Glider in a Near-Shore Marine Environment

TL;DR: This paper investigates the use of an acoustic glider to perform acoustical meteorology, and proposes a conversion relationship between sound pressure level and wind speed, taking the form of an outlier-robust nonlinear regression model learned with in situ data.