Analyses of sex and individual differences in vocalizations of Australasian gannets using a dynamic time warping algorithm

Krull, Cheryl R.; Ranjard, Louis; Landers, Todd J.; Ismar, Stefanie M. H.; Matthews, Jennifer L.; Hauber, Márk E.

doi:10.1121/1.4734237

Cited by 23 publications

(19 citation statements)

References 46 publications

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“…Moreover, sound parametric representations, for example Mel frequency cepstral coefficients, used for speech signal processing are useful in bioacoustics settings, in particular for bird vocalisation analyses (Krull et al . ; Ranjard et al . ).…”

Section: Introductionmentioning

confidence: 99%

MatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models

et al. 2016

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Summary1. Passive bioacoustic recording devices are now widely available and able to continuously record remotely located sites for extended periods, offering great potential for wildlife monitoring and management. Analysis of the huge data sets generated, in particular for specific biotic sound recognition, remains a critical bottleneck for widespread adoption of these technologies as current methods are labour intensive. 2. Several methods borrowed from speech processing frameworks, such as hidden Markov models, have been successful in analysing bioacoustic data, but the software implementations can be expensive and difficult to use for non-specialists involved in wildlife conservation. To remedy this, we present a software interface to a popular speech recognition system making it possible for non-experts to implement hidden Markov models for bioacoustic signal processing. Octave/Matlab functions are used to simplify the set-up and the definition of a bioacoustic signal recogniser as well as the analysis of the results. 3. We present the different functions as a workflow. To demonstrate how the package can be used, we give the results of an analysis of a bioacoustic monitoring data set to detect the nocturnal presence and behaviour of a cryptic seabird species, the common diving petrel Pelecanoides urinatrix urinatrix, from Northern New Zealand. 4. We show that the package MatlabHTK can be used efficiently to reconstruct the daily patterns of colony activity in the common diving petrel.

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Section: Introductionmentioning

confidence: 99%

MatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models

et al. 2016

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“…; Krull et al . ), and is related to the better known dynamic time warping algorithm (Buck & Tyack ). However, LD itself remains somewhat unknown in the field of animal acoustic communication.…”

Section: Methodsmentioning

confidence: 99%

Quantifying similarity in animal vocal sequences: which metric performs best?

Kershenbaum

Garland

2015

Methods Ecol Evol

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n-gram models, repeat distribution, and edit distance), and data generated by different stochastic 30 processes (entropy rate and n-grams). However, the string edit (Levenshtein) distance performed 31 consistently and significantly better than all other tested metrics (including entropy, Markov 32 chains, n-grams, mutual information) for all empirical datasets, despite being less commonly used 33 in the field of animal acoustic communication.

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“…As well as cetaceans, DTW has been widely used for analysing the vocalisations of birds [30], and other mammals [31]. This technique gives improved matching particularly when salient features in the whistle profile (such as peaks) may occur at slightly different times (Figure 2).…”

Section: Methodsmentioning

confidence: 99%

The Encoding of Individual Identity in Dolphin Signature Whistles: How Much Information Is Needed?

2013

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Bottlenose dolphins (Tursiops truncatus) produce many vocalisations, including whistles that are unique to the individual producing them. Such “signature whistles” play a role in individual recognition and maintaining group integrity. Previous work has shown that humans can successfully group the spectrographic representations of signature whistles according to the individual dolphins that produced them. However, attempts at using mathematical algorithms to perform a similar task have been less successful. A greater understanding of the encoding of identity information in signature whistles is important for assessing similarity of whistles and thus social influences on the development of these learned calls. We re-examined 400 signature whistles from 20 individual dolphins used in a previous study, and tested the performance of new mathematical algorithms. We compared the measure used in the original study (correlation matrix of evenly sampled frequency measurements) to one used in several previous studies (similarity matrix of time-warped whistles), and to a new algorithm based on the Parsons code, used in music retrieval databases. The Parsons code records the direction of frequency change at each time step, and is effective at capturing human perception of music. We analysed similarity matrices from each of these three techniques, as well as a random control, by unsupervised clustering using three separate techniques: k-means clustering, hierarchical clustering, and an adaptive resonance theory neural network. For each of the three clustering techniques, a seven-level Parsons algorithm provided better clustering than the correlation and dynamic time warping algorithms, and was closer to the near-perfect visual categorisations of human judges. Thus, the Parsons code captures much of the individual identity information present in signature whistles, and may prove useful in studies requiring quantification of whistle similarity.

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Analyses of sex and individual differences in vocalizations of Australasian gannets using a dynamic time warping algorithm

Cited by 23 publications

References 46 publications

MatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models

MatlabHTK: a simple interface for bioacoustic analyses using hidden Markov models

Quantifying similarity in animal vocal sequences: which metric performs best?

The Encoding of Individual Identity in Dolphin Signature Whistles: How Much Information Is Needed?

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