Computer models for masked hearing experiments with beluga whales (<i>Delphinapterus leucas</i>)

Erbe, Christine; King, Andrew; Yedlin, M.; Farmer, David M.

doi:10.1121/1.426945

Cited by 15 publications

(6 citation statements)

References 18 publications

(20 reference statements)

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“…Groups which are linked on the left-hand side of the graphs spend more time traveling together than those linked on the right-hand side. Spong et al, 1993;Erbe et al, in press͒, amplitude information is excluded from the analysis of frequency contours. Although this may be a disadvantage in some studies analyzing recordings obtained in controlled environments, it will prove beneficial in others where differences in recording equipment and in the composition of background noise introduces spurious variability into the data.…”

Section: Discussionmentioning

confidence: 99%

Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects

Deecke

Ford

Spong³

1999

The Journal of the Acoustical Society of America

118

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A quantitative measure of acoustic similarity is crucial to any study comparing vocalizations of different species, social groups, or individuals. The goal of this study was to develop a method of extracting frequency contours from recordings of pulsed vocalizations and to test a nonlinear index of acoustic similarity based on the error of an artificial neural network at classifying them. Since the performance of neural networks depends on the amount of consistent variation in the training data, this technique can be used to assess such variation from samples of acoustic signals. The frequency contour extraction and the neural network index were tested on samples of one call type shared by nine social groups of killer whales. For comparison, call similarity was judged by three human subjects in pairwise classification tasks. The results showed a significant correlation between the neural network index and the similarity ratings by the subjects. Both measures of acoustic similarity were significantly correlated with the groups' association patterns, indicating that both methods of quantifying acoustic similarity are biologically meaningful. An index based on neural network analysis therefore represents an objective and repeatable means of measuring acoustic similarity, and allows comparison of results across studies, species and time.

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Section: Discussionmentioning

confidence: 99%

Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects

Deecke

Ford

Spong³

1999

The Journal of the Acoustical Society of America

118

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“…The ability to reliably detect acoustic events depends largely on the structure of the recording environment, the signal‐to‐noise ratio (SNR) and the complexity and variability of the signals to be detected. For simple or complex sounds with low variability, template‐based detection algorithms using spectrogram correlation (Mellinger & Clark 2000) or matched filters (Erbe et al. 1999) can perform quite well, even in higher noise environments.…”

Section: Signal Recognitionmentioning

confidence: 99%

Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus

Blumstein¹,

Mennill

Clemins³

et al. 2011

Journal of Applied Ecology

519

398

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Summary1. Animals produce sounds for diverse biological functions such as defending territories, attracting mates, deterring predators, navigation, finding food and maintaining contact with members of their social group. Biologists can take advantage of these acoustic behaviours to gain valuable insights into the spatial and temporal scales over which individuals and populations interact. Advances in bioacoustic technology, including the development of autonomous cabled and wireless recording arrays, permit data collection at multiple locations over time. These systems are transforming the way we study individuals and populations of animals and are leading to significant advances in our understandings of the complex interactions between animals and their habitats. 2. Here, we review questions that can be addressed using bioacoustic approaches, by providing a primer on technologies and approaches used to study animals at multiple organizational levels by ecologists, behaviourists and conservation biologists. 3. Spatially dispersed groups of microphones (arrays) enable users to study signal directionality on a small scale or to locate animals and track their movements on a larger scale. 4. Advances in algorithm development can allow users to discriminate among species, sexes, age groups and individuals. 5. With such technology, users can remotely and non-invasively survey populations, describe the soundscape, quantify anthropogenic noise, study species interactions, gain new insights into the social dynamics of sound-producing animals and track the effects of factors such as climate change and habitat fragmentation on phenology and biodiversity. 6. There remain many challenges in the use of acoustic monitoring, including the difficulties in performing signal recognition across taxa. The bioacoustics community should focus on developing a *Correspondence author. E-mail: marmots@ucla.edu 2011, 48, 758-767 doi: 10.1111/j.1365-2664.2011.01993.x Ó 2011 The Authors. Journal of Applied Ecology Ó 2011 British Ecological Society common framework for signal recognition that allows for various species' data to be analysed by any recognition system supporting a set of common standards. 7. Synthesis and applications. Microphone arrays are increasingly used to remotely monitor acoustically active animals. We provide examples from a variety of taxa where acoustic arrays have been used for ecological, behavioural and conservation studies. We discuss the technologies used, the methodologies for automating signal recognition and some of the remaining challenges. We also make recommendations for using this technology to aid in wildlife management. Journal of Applied Ecology

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“…Erbe et al 8 estimated critical bandwidths for beluga whales from critical ratio data. 9 It was shown that critical bands were about 1 12 of an octave wide in the frequency range of interest.…”

Section: Calculation Of the Zone Of Audibilitymentioning

confidence: 99%

Zones of impact around icebreakers affecting beluga whales in the Beaufort Sea

Erbe¹,

Farmer²

2000

The Journal of the Acoustical Society of America

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A software model estimating zones of impact on marine mammals around man-made noise [C. Erbe and D. M. Farmer, J. Acoust. Soc. Am. 108, 1327-1331 (2000)] is applied to the case of icebreakers affecting beluga whales in the Beaufort Sea. Two types of noise emitted by the Canadian Coast Guard icebreaker Henry Larsen are analyzed: bubbler system noise and propeller cavitation noise. Effects on beluga whales are modeled both in a deep-water environment and a near-shore environment. The model estimates that the Henry Larsen is audible to beluga whales over ranges of 35-78 km, depending on location. The zone of behavioral disturbance is only slightly smaller. Masking of beluga communication signals is predicted within 14-71-km range. Temporary hearing damage can occur if a beluga stays within 1-4 km of the Henry Larsen for at least 20 min. Bubbler noise impacts over the short ranges quoted; propeller cavitation noise accounts for all the long-range effects. Serious problems can arise in heavily industrialized areas where animals are exposed to ongoing noise and where anthropogenic noise from a variety of sources adds up.

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Computer models for masked hearing experiments with beluga whales (Delphinapterus leucas)

Cited by 15 publications

References 18 publications

Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects

Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects

Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus

Zones of impact around icebreakers affecting beluga whales in the Beaufort Sea

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