An analysis of pilot whale vocalization activity using hidden Markov models

Valentin Mina Popov, Roland Langrock, Stacy Lynn De Ruiter, Fleur Visser

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
1 Downloads (Pure)


Vocalizations of cetaceans form a key component of their social interactions. Such vocalization activity is driven by the behavioral states of the whales, which are not directly observable, so that latent-state models are natural candidates for modeling empirical data on vocalizations. In this paper, we use hidden Markov models to analyze calling activity of long-finned pilot whales (Globicephala melas) recorded over three years in the Vestfjord basin off Lofoten, Norway. Baseline models are used to motivate the use of three states, while more complex models are fit to study the influence of covariates on the state-switching dynamics. Our analysis demonstrates the potential usefulness of hidden Markov models in concisely yet accurately describing the stochastic patterns found in animal communication data, thereby providing a framework for drawing meaningful biological inference.
Original languageEnglish
Pages (from-to)159-171
JournalJournal of the Acoustical Society of America
Issue number1
Early online date12 Jan 2017
Publication statusPublished - Jan 2017


Dive into the research topics of 'An analysis of pilot whale vocalization activity using hidden Markov models'. Together they form a unique fingerprint.

Cite this