## 1 Introduction  
Sperm whales _(Physeter macrocephalus)_, when living in a social community, often forage in small groups. During their feeding dive, which may be to depths up to 2 kilometres [1], they start producing sonar signals fairly soon after the start of a dive and generally continue until the ascent back to the surface. Usually one click per second is produced on average, but at times this frequency is increased (presumably when they have found prey) and up to fifty signals per second may be produced [2]. Thissequence of very rapid clicks is called a creak, and the period from the start of a click sequence to a crack, or a prolonged moment of silence, is called a click train. The recording of these diving groups results in a mixture of signals, and the manual assignment of a click from a click train to the animal that produced it is often a difficult and arduous task. To this end, we want to build an automated method that can distinguish between clicks from different animals using primarily characteristic information in the clicks themselves. This could then be combined with, for example, time delays of arrival at the hydrophones to reliably reconstruct the original click trains for the individual whales. When recorded on axis, a click consist of a series of up to five pulses separated by a few milliseconds where the second pulse dominates strongly over the others. A relative difference of 40 dB in signal level can be encountered [3]. The clicks are broad band with energy in frequencies exceeding 30 kHz and a peak frequency around 12 kHz. However, when recording off axis, as is almost always the case in practice, the signal becomes distorted with strong attenuation of this second pulse. The click's characteristics may change depending on the animal's orientation and distance to the hydrophone, while the animal's depth [4] and activities may play a role as well. A typical example of a low-pass filtered clicks is shown in Figure 1. Each image in this figure contains 10 consecutive clicks superimposed on each other. Synchronisation was done with a low frequency matched filter. It can be seen how the signal changes through the click train. Focusing on for example the area around sample 210, a new peak appears probably due to different time delays between the click's pulses that can be caused by changes in the animal's orientation with respect to the hydrophone. Research has shown [5] that dominant frequencies of off axis clicks for both male and female sperm whales can be found below 2,000 Hz. Since low frequencies are less influenced by orientation or distance, they are more suitable to be searched for constant characteristics. An earlier attempt has been made to identify a whale based on modelling these dominant frequencies directly using a Gabor function [6]. The use of Gabor functions is interesting as it suggests that nature uses a signal optimised in a time-frequency sense, and they have been used successfully to describe dolphin sonar [7, 8]. However, it was found that the dominant frequencies were not stable enough to be used as an identifier for the entire duration of a dive. Similarly, in the same study, a simple linear classification method was not able to distinguish clearly between individuals in a small group of whales, and therefore this report looks at non-linear classifiers in the form of a neural network. Neural networks  
Figure 1: Example clicks from one animal at different moments in the click train. Each image contains 10 consecutive superimposed clicks that were filtered below 3,000 Hz and normalised in energy. A pulse seems to be moving from left to right, especially visible around sample 210.  
have been used in the past for marine mammal classification with some success [9; 10]. The distribution of the characteristics from the sperm whale clicks suggested the use of a Gaussian model, and therefore a radial basis function network (RBF) architecture was used in [11] to separate sperm whales.