PUB - Publikationen an der Universität Bielefeld

This paper presents a new sonification model for the exploration of topographically ordered high-dimensional data (multi-parameter maps, volume data) where each data item consists of a position and feature vector. The sonification model implements a common metaphor from thermodynamics that heat can be interpreted as stochastic motion of 'molecules'. The latter are determined by the data under examination, and 'live' only in the feature space. Heat-induced interactions cause acoustic events that fuse to a granular sound texture which conveys meaningful information about the underlying distribution in feature space. As a second ingredient of the model, data selection is achieved by a separated navigation process in position space using a dynamic aura model, such that heat can be induced locally. Both, a visual and an auditory display are driven by the underlying model. We exemplify the sonification by means of interaction examples for different high-dimensional distributions.