## 5 Conclusion  
We showed that separation of sperm whale sonar clicks using a Gaussian kernel with support vector machines, or radial basis functions, has the potential to work well on single click trains. It is not yet clear if the features are constant enough to allow their use during an entire dive. While we found good performance on the single dive we had available, this may not be the case when this type of data is available from all whales. It also has to be kept in mind that these data came from individually diving whales, and not from a group. Unfortunately, we did not have group data available that allowed separation of the individual animals with certainty. If the features evolve during the dive then it may be necessary to look for a method that can adapt itself to these gradual changes. In the case of SVM, drawbacks for real-time usage may be the use of optimisation routines for learning, as well as the amount of necessary support vectors and the number of machines which slow down its execution. On the other hand, radial basis function classification required less information to obtain similar results and its fast training stage may allow it to adapt more easily to a changing environment. However, it showed slightly worse generalisation capacity.  
An important detail of the proposed classification methods is that they have to be trained with already separated training data. This is not necessarily a problem when classification is done off-line and the training sets can be manually selected, but when used in real-time this part needs to be done automatically as well. This would require at least an estimate of the number of animals diving at that instant, which could be done with unsupervised clustering methods. Results to this end were obtained by [29], using spectral clustering on the first two cepstral coefficients and the slope of the onset of a click. Another study [30] used a self organising map to cluster data using sperm whale codas. While these are never emitted during a foraging dive, a similar technique might be applicable on regular clicks.  
The classification process itself could be improved in various ways. The local discriminant basis is selected on the requirement that it can reconstruct a signal. However, we are not interested in the reconstructed signal, and therefore we can use a much wider range of wavelets (e.g., non-orthogonal) and different time-frequency bin selection procedures. Other issues are the synchronisation of the clicks,which generally presented a small error that can affect the wavelet coefficients, and the selection of the characteristics. The strongest features are now chosen with [PERSON]'s discriminant, but the classification itself is based on non-linear dependencies between the features. A similar non-linear measure for the feature strength might result in a better selection.  
It is possible that other data sets cannot use the same local basis. Different noise patterns and environments may lead to a different LDB selection. Even when the basis is the same, the strongest coefficients may not be the same as ones selected in this study. Therefore, it is probably not possible to define a fixed basis with features that can be universally applied, and these will have to be re-evaluated for every recording. More data will also be necessary to gain better insights into the variability of the click features during an entire dive, and especially to investigate the possibility of using similar algorithms for unique identification of an animal at different times and in different environments.