Local Passive Acoustic Monitoring of Narwhal Presence in the Canadian Arctic: A Pilot Project

Long-term community-based monitoring of narwhals (Monodon monoceros) is needed because narwhals are important to local Inuit and are facing changes in their environment. We examined the suitability of passive acoustic recording for monitoring narwhals, using data gathered in the Canadian Arctic from an autonomous acoustic recorder (Repulse Bay, 2006) and a hand-held digital recorder (Koluktoo Bay, 2006 – 08). We found a relationship between the number of narwhals observed passing a fixed point and the number of calls heard. In addition, we found that an automated call detector could isolate segments of recording containing narwhal vocalizations over long recording periods containing non-target sound, thus decreasing the time spent on the analysis. Collectively, these results suggest that combining passive acoustic sampling with an automated call detector offers a useful approach for local monitoring of the presence and relative abundance of narwhals.La nécessité d’avoir un programme communautaire de surveillance à long terme des narvals (Monodon monoceros) s’avère évidente étant donné que les narvals revêtent de l’importance aux yeux des Inuits de la région et que leur environ­nement est en pleine évolution. Nous explorons la pertinence d’un programme de surveillance par acoustique passive pour les populations de narvals à partir de données récoltées dans l’Arctique canadien à l’aide d’une enregistreuse autonome (Repulse Bay, 2006) et d’une enregistreuse portable (Koluktoo Bay, 2006 – 2008). Grâce à des enregistrements accompagnés d’obser­vations sur le terrain, nous avons trouvé une corrélation entre le nombre de vocalisations entendues et le nombre de narvals observés. L’utilisation d’un détecteur automatique de vocalisations de narvals a permis d’isoler des segments d’enregis­trements contenant des vocalisations de narvals sur de longues périodes d’enregistrement contenant des sons non-ciblés, et ainsi diminuer le temps d’analyse. Ces résultats suggèrent que la combinaison de surveillance acoustique passive avec l’utili­sation d’un détecteur automatique offre une approche utile pour la surveillance locale de la présence et de l’abondance relative des narvals

Comparing call-based versus subunit-based methods for categorizing Norwegian killer whale, Orcinus orca, vocalizations

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Animal Behaviour 81 (2011): 377-386, doi:10.1016/j.anbehav.2010.09.020.Students of animal communication face significant challenges when deciding how to categorise calls into subunits, calls, and call series. Here, we use algorithms designed to parse human speech to test different approaches for categorising calls of killer whales. Killer whale vocalisations have traditionally been categorised by humans into discrete call types. These calls often contain internal spectral shifts, periods of silence, and synchronously produced low and high frequency components, suggesting that they may be composed of subunits. We describe and compare three different approaches for modelling Norwegian killer whale calls. The first method considered the whole call as the basic unit of analysis. Inspired by human speech processing techniques, the second and third methods represented the calls in terms of subunits. Subunits may provide a more parsimonious approach to modelling the vocal stream since (1) there were fewer subunits than call types; (2) nearly 75% of all call types shared at least one subunit. We show that contour traces from stereotyped Norwegian killer whale calls yielded similar automatic classification performance using either whole calls or subunits. We also demonstrate that subunits derived from Norwegian stereotyped calls were detected in some Norwegian variable (non-stereotyped) calls as well as the stereotyped calls of other killer whale populations. Further work is required to test whether killer whales use subunits to generate and categorize their vocal repertoire.The undergraduate students were supported by the Massachusetts Institute of Technology Undergraduate Research Opportunities Program office and the Ocean Life Institute (OLI) at the Woods Hole Oceanographic Institution (WHOI). Field work was financed by the OLI, National Geographic Society and WWF Sweden. A. D. Shapiro was funded by a National Defense Science and Engineering Graduate Fellowship and the WHOI Academic Programs Office

Segmentation of Killer Whale Vocalizations Using the Hilbert-Huang Transform

The study of cetacean vocalizations is usually based on spectrogram analysis. The feature extraction is obtained from 2D methods like the edge detection algorithm. Difficulties appear when signal-to-noise ratios are weak or when more than one vocalization is simultaneously emitted. This is the case for acoustic observations in a natural environment and especially for the killer whales which swim in groups. To resolve this problem, we propose the use of the Hilbert-Huang transform. First, we illustrate how few modes (5) are satisfactory for the analysis of these calls. Then, we detail our approach which consists of combining the modes for extracting the time-varying frequencies of the vocalizations. This combination takes advantage of one of the empirical mode decomposition properties which is that the successive IMFs represent the original data broken down into frequency components from highest to lowest frequency. To evaluate the performance, our method is first applied on the simulated chirp signals. This approach allows us to link one chirp to one mode. Then we apply it on real signals emitted by killer whales. The results confirm that this method is a favorable alternative for the automatic extraction of killer whale vocalizations

Automatic Detectors for Underwater Soundscape Measurements

Environmental impact regulations require that marine industrial operators quantify their contribution to underwater noise scenes. Automation of such assessments becomes feasible with the successful categorisation of sounds into broader classes based on source types – biological, anthropogenic and physical. Previous approaches to passive acoustic monitoring have mostly been limited to a few specific sources of interest. In this study, source-independent signal detectors are developed and a framework is presented for the automatic categorisation of underwater sounds into the aforementioned classes