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

Overview of attention for article published in PLOS ONE, October 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
5 news outlets
twitter
16 X users
facebook
5 Facebook pages
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
59 Dimensions

Readers on

mendeley
205 Mendeley
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Title
The Encoding of Individual Identity in Dolphin Signature Whistles: How Much Information Is Needed?
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0077671
Pubmed ID
Authors

Arik Kershenbaum, Laela S. Sayigh, Vincent M. Janik

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 205 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 <1%
Switzerland 1 <1%
Netherlands 1 <1%
Italy 1 <1%
Australia 1 <1%
South Africa 1 <1%
India 1 <1%
United Kingdom 1 <1%
Mexico 1 <1%
Other 2 <1%
Unknown 193 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 20%
Student > Ph. D. Student 37 18%
Student > Master 37 18%
Student > Bachelor 20 10%
Other 16 8%
Other 32 16%
Unknown 21 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 106 52%
Environmental Science 30 15%
Computer Science 8 4%
Engineering 5 2%
Earth and Planetary Sciences 5 2%
Other 22 11%
Unknown 29 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 57. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 January 2021.
All research outputs
#760,604
of 25,866,425 outputs
Outputs from PLOS ONE
#10,056
of 225,574 outputs
Outputs of similar age
#6,486
of 225,732 outputs
Outputs of similar age from PLOS ONE
#274
of 5,161 outputs
Altmetric has tracked 25,866,425 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 225,574 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 225,732 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 5,161 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.