Motion-Sound Mapping through Interaction: An Approach to User-Centered Design of Auditory Feedback Using Machine Learning.

Technologies for sensing movement are expanding toward everyday use in virtual reality, gaming, and artistic practices. In this context, there is a need for methodologies to help designers and users create meaningful movement experiences. This article discusses a user-centered approach for the design of interactive auditory feedback using interactive machine learning. We discuss Mapping through Interaction, a method for crafting sonic interactions from corporeal demonstrations of embodied associations between motion and sound. It uses an interactive machine learning approach to build the mapping from user demonstrations, emphasizing an iterative design process that integrates acted and interactive experiences of the relationships between movement and sound. We examine Gaussian Mixture Regression and Hidden Markov Regression for continuous movement recognition and real-time sound parameter generation. We illustrate and evaluate this approach through an application in which novice users can create interactive sound feedback based on coproduced gestures and vocalizations. Results indicate that Gaussian Mixture Regression and Hidden Markov Regression can efficiently learn complex motion-sound mappings from few examples.