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

Arik Kershenbaum,Vincent M Janik,Laela S Sayigh,Martine Hausberger

doi:10.1371/journal.pone.0077671

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.

Highlights

The complexity of dolphin vocalisations has long fascinated scientists, and inspired numerous attempts to classify and decode them
A post-hoc Tukey HSD test showed that both Parsons code metrics performed significantly better than the dynamic time-warping (DTW) or correlation metric (CM) techniques (Figure 6) for each of the clustering algorithms (p,0.001 in each case)
The DTW metric performance was better than CM using the Adaptive Resonance Theory (ART) clustering, slightly worse than CM using k-means, and no different with hierarchical clustering

Summary

Introduction

The complexity of dolphin vocalisations has long fascinated scientists, and inspired numerous attempts to classify and decode them. Dolphins respond preferentially to the signature whistles of familiar individuals [7], and playback experiments with artificially generated sounds have shown that animals can distinguish between the signature calls of different individuals using only the frequency modulation profile of the tonal elements in the call [13]. This contrasts with the mechanism of individual recognition in many other species, in which individuals use information encoded in acoustic parameters such as call length, scalar measures of fundamental frequency, and harmonic composition; e.g. red deer [8] and rock hyrax [9]. It is known that dolphins use the whistle frequency modulation as a cue in recognition, it is not known what features of the whistle modulation encode individual identity

Methods

Results

Conclusion