Abstract:

This study reports the use of unsupervised, self-organizing neutral networks to categorize the repertoire of false killer whale vocalizations. Self-organizing networks are capable of detecting patterns in their input and partitioning those patterns into categories without requiring that the number or types of categories be predefined. The inputs for the neural networks were two-dimensional characterizations of false killer whale localizations where each vocalization was characterized by modulations in duty cycle and peak frequency [Murray et al. (in prep.)]. The first neural network used competitive learning, where neurons in a competitive layer distributed themselves to recognize frequently presented input vectors. This network resulted in classes representing typical patterns in the vocalizations. The second network was a Kohonen feature map which organized the outputs topologically, providing a graphical organization of pattern relationships. The networks performed well as measured by (1) the average correlation between the input vectors and the weight vectors for each category, and (2) the ability of the networks to classify novel localizations. The techniques used in this study could easily be applied to other species and facilitate the development of objective, comprehensive repertoire models.