Probabilistic Models For Designing Motion And Sound Relationships

Abstract

We present a set of probabilistic models that support the design of movement and sound relationships in interactive sonic systems. We focus on a mapping--by--demonstration approach in which the relationships between motion and sound are defined by a machine learning model that learns from a set of user examples. We describe four probabilistic models with complementary characteristics in terms of multimodality and temporality. We illustrate the practical use of each of the four models with a prototype application for sound control built using our Max implementation.

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