Discrimination of individual tigers (<i>Panthera tigris</i>) from long distance roars

Ji, An; Johnson, Michael T.; Walsh, Edward J.; McGee, JoAnn; Armstrong, Douglas L.

doi:10.1121/1.4789936

Cited by 22 publications

(18 citation statements)

References 37 publications

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“…Spectrograms (consisting of discrete Fourier transforms of short, frequently overlapped, segments of the signal) are ubiquitous and characterise well those acoustic features related to spectral profile and frequency modulation, many of which are relevant in animal acoustic communication. Examples of such features include minimum and maximum fundamental frequency, slope of the fundamental frequency, number of inflection points, and the presence of harmonics (Oswald et al ., ) that vary, for example, between individuals (Buck & Tyack, ; Blumstein & Munos, ; Koren & Geffen, ; Ji et al ., ; Kershenbaum, Sayigh & Janik, ; Root‐Gutteridge et al ., ), and in different environmental and behavioural contexts (Matthews et al ., ; Taylor, Reby & McComb, ; Henderson, Hildebrand & Smith, ).…”

Section: Acoustic Unitsmentioning

confidence: 99%

Acoustic sequences in non‐human animals: a tutorial review and prospectus

et al. 2014

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Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, “Analysing vocal sequences in animals”. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.

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Section: Acoustic Unitsmentioning

confidence: 99%

Acoustic sequences in non‐human animals: a tutorial review and prospectus

et al. 2014

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“…Although this can be done automatically using state of the art deep learning techniques (Stowell et al 2018), acoustic features are often extracted manually, whereby simple summary variables (e.g. min, max and mean) associated with the fundamental frequency and the harmonics are calculated (Fan et al 2019;Ji et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The recognition models are assessed based on their ability to classify the 'unseen' calls correctly. Several algorithms have been used for this purpose, including discriminant functions (Blumstein and Munos 2005;Fan et al 2019), artificial neural networks (Mielke and Zuberbühler 2013;Reby et al 1998), gaussian mixture models (Cheng et al 2010) and hidden Markov models (Clemins et al 2005;Ji et al 2013). One of the main issues affecting the classification performance of vocal recognition models is the presence of background noise.…”

Section: Introductionmentioning

confidence: 99%

Vocal discrimination of African lions and its potential for collar-free tracking

et al. 2020

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Previous research has shown that African lions (Panthera leo) have the ability to discriminate between conspecific vocalisations, but little is known about how individual identity is conveyed in the spectral structure of roars. Using acoustic -accelerometer biologgers that allow vocalisations to be reliably associated with individual identity, we test for vocal individuality in the fundamental frequency (f0) of roars from 5 male lions, firstly by comparing simple f0 summary features and secondly by modelling the temporal pattern of the f0 contour. We then assess the application of this method for discriminating between individuals using passive acoustic monitoring. Results indicate that f0 summary features only allow for vocal discrimination with 70.7% accuracy. By comparison, vocal discrimination can be achieved with an accuracy of 91.5% based on individual differences in the temporal pattern of the f0 sequence. We further demonstrate that passively recorded lion roars can be localised and differentiated with similar accuracy. The existence of individually unique f0 contours in lion roars and their relatively lower attenuation indicates a likely mechanism enabling individual lions to identify conspecifics over long distances. These differences can be exploited by researchers to track individuals across the landscape and thereby supplement conventional lion monitoring approaches.

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“…Notably, the distinctive alarm calls of some species are acoustically unique to different types of danger171819. Other studies have exemplified the ability to distinguish between emitting individuals based on acoustic features of their vocalizations202122232425262728293031, while several studies, mainly in primates, revealed subtle context-dependent acoustic modifications of calls in everyday behaviors, such as food-related behaviors32, agonistic interactions33, and long-distance calls29.…”

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confidence: 99%

Everyday bat vocalizations contain information about emitter, addressee, context, and behavior

Prat

Taub

Yovel

2016

Sci Rep

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Animal vocal communication is often diverse and structured. Yet, the information concealed in animal vocalizations remains elusive. Several studies have shown that animal calls convey information about their emitter and the context. Often, these studies focus on specific types of calls, as it is rarely possible to probe an entire vocal repertoire at once. In this study, we continuously monitored Egyptian fruit bats for months, recording audio and video around-the-clock. We analyzed almost 15,000 vocalizations, which accompanied the everyday interactions of the bats, and were all directed toward specific individuals, rather than broadcast. We found that bat vocalizations carry ample information about the identity of the emitter, the context of the call, the behavioral response to the call, and even the call's addressee. Our results underline the importance of studying the mundane, pairwise, directed, vocal interactions of animals.Many animal species use vocal communication 1 , but the chaotic nature of the acoustics of these vocalizations often complicates their cataloging into clearly divided types and contexts 2 . Understanding the encapsulated information in animal vocalizations is central to the study of sociality, communication, and language evolution. Yet, in the research of nonhuman animals, the purpose and meaning of the vocal message often remain obscure. Researchers of animal communication, seeking homology to semantics, may relate behavioral observations to acoustic measurements, and thus reveal some of the information content of vocal expressions. Indeed, several studies have described cases of vocalizations as functionally referential, i.e. signals which are both specific to a certain context and elicit an appropriate response by a receiver 3-5 . However, the requisite for a specific response might be too strict, ignoring many other information bearing vocalizations. Many vocalizations evoke a response according to their context or their receiver's internal state 6 , and not solely based on their acoustics. Thus, the information capacity of animal vocalizations should still be explored, even when interpreting the behavioral responses is not straightforward 7 . Identifying context specific calls can be a first step toward the recovering of meaning in animal communication. For example, many studies indicated the existence of context specific vocalizations in various taxa (e.g., birds 8-10 , cetaceans 11,12 , dogs 13 , bats 14,15 , and nonhuman primates 16 ). Notably, the distinctive alarm calls of some species are acoustically unique to different types of danger [17][18][19] . Other studies have exemplified the ability to distinguish between emitting individuals based on acoustic features of their vocalizations 20-31 , while several studies, mainly in primates, revealed subtle context-dependent acoustic modifications of calls in everyday behaviors, such as food-related behaviors 32 , agonistic interactions 33 , and long-distance calls 29 .Bats are social mammals that often roost in very large colonies,...

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Discrimination of individual tigers (Panthera tigris) from long distance roars

Cited by 22 publications

References 37 publications

Acoustic sequences in non‐human animals: a tutorial review and prospectus

Acoustic sequences in non‐human animals: a tutorial review and prospectus

Vocal discrimination of African lions and its potential for collar-free tracking

Everyday bat vocalizations contain information about emitter, addressee, context, and behavior

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