Washington: Scientists have developed a new method to analyse the unique "tune" of a dolphin`s signature whistle.
Researchers from The National Institute for Mathematical and Biological Synthesis (NIMBioS) in US have applied the algorithm used to find tunes in music retrieval systems to identify the signature whistles of dolphins.
Bottlenose dolphins, in particular, recognise each other by name: the sound of each animal`s "signature" whistle, which each dolphin develops at a young age.
Bottlenose dolphins appear to show preference to the signature whistles of familiar individuals. Scientists have found that dolphins use the signature whistles to foster and maintain group cohesion.
Until now, the classification of individual dolphin whistles has typically been done by examining a spectrograph, which visually represents the spectrum of frequencies found in a sound.
But the method is time-consuming, requires more data than might be necessary, and is subject to human error.
Researchers found a new method for identification that uses an algorithm based on what`s called the Parsons code, which has been used extensively in computerised retrieval of tunes from music databases.
Instead of looking at the precise variation in frequency, the Parsons code only considers whether at each point in time the pitch goes up, down, or stays the same.
The researchers examined 400 signature whistles from 20 different dolphins.
The new algorithm did well at assigning signature whistles to individual animals, helping scientists to classify the tested whistles quickly and efficiently, according to the study.
"The Parsons code is a robust way to compare dolphins` signature whistles because it is able to home in on the variation in frequency that actually matters. It discards the information that isn`t useful for the analysis," said lead author Arik Kershenbaum, a postdoctoral fellow at NIMBioS.
"Cetacean vocalisations are highly varied and presumably serve varied functions. So determining what aspects of the vocalisations hold information is crucial to be able to classify them and to be able to understand their meaning," Kershenbaum said.
The study was published in the journal PLOS ONE.