Dolphin whistle classification for determining group identities

Traditionally, dolphin recognition techniques in the field have relied upon photographic identification, but this has several practical disadvantages. Some whistled vocalisations may be used for group identification, and these are viable at longer ranges than visual means. Novel automated algorithms have been developed to detect, encode and classify these whistles, with the aim of allowing a rapid, quantitative assessment of group identity. Hidden Markov models were constructed for each whistle class together with statistical representations of the whistles' detailed shapes, in an unsupervised manner and from little a priori information. The encoding and classification routines were applied to whistles from a 15 min recording made during a field trial, which contained three periods of whistle activity. Cross-group comparison of the whistle classes suggested that the whistles from the first period were vocally distinct from the second and third. Further analysis revealed that the latter two periods contained whistles that had been recorded simultaneously from two separate groups, but which could indeed be separated with the classification routines. This paper will detail the problems involved with detecting the whistles, characterising and classifying them, and finally will show the analysis of the results to calculate group similarity probabilities.