CHARACTERIZATION OF DOLPHIN ACOUSTIC ECHOLOCATION DATA USING A DYNAMICAL CLASSIFICATION METHOD

Many methods for time series analysis derived from nonlinear dynamical systems theory have been developed in the last decade, and have demon strated remarkable results in a variety of simulated, experimental, an d real applications. Classification of time series based on the underl ying dynamical generator is also potentially powerful, and we have pre viously presented a method for dynamical classification based on empir ically estimated sets of nonlinear ordinary differential equations, i. e. global dynamical models. A particularly useful area of application for such methods may be biologic and medical data analysis, where few quantitative methods exist for the highly complex time evolutions. Her e, as an example of the application of such classification methods, we present an analysis of data of the acoustic pulse trains produced by dolphins as they attempt to echo-locate objects in an ocean environmen t, which is derived from a controlled experimental framework.