Once extracted and measured using Raven and PAMGuard, whistle contours are categorised into types. This is done using a MATLab-based programme called ARTwarp.
ARTwarp is a MATLAB-based program that uses an ART2 neural network to categorize tonal animal sounds. These sounds are represented by frequency contours obtained by analysing their audio spectrograms in Raven and PAMGuard.
ARTwarp’s Dynamic Time Warping algorithm allows whistles to be temporally stretched or squished. This allows broader similarities between whistle contours to be captured. Hence, ARTWarp allows us to determine how many types of whistle dolphins produce. It also counts how many whistle types occur. This allows us to compare differences in whistle repertoire between locations.
The interactive user interface displays various details of the real-time processing taking place (Figure 3). Each sound category or neuron is represented by a reference frequency contour, to which new whistles are compared to. If the new whistle matches the reference contour of a particular category according to a set vigilance (or similarity) level, then the new whistle is added to that category. If not, the whistle creates its own new category.
The VIP aims to improve ARTwarp’s user interface and tweak its network parameters. The improvements and analyses conducted will help inform dolphin conservation and management strategies.