Improving data-driven design and exploration of digital musical instruments
Christopher Laguna, Rebecca Fiebrink · 2014
We present Gesture Mapper, an application for digital musical instrument designers to rapidly prototype mappings from performer gestures to sound synthesis parameters. Prior work [2] has shown that using interactive supervised learning to generate mappings from user-generated examples can be more efficient and effective than users writing mapping functions in code. In this work, we explore new ways to improve on data-driven design of interactive systems, specifically by proposing new mechanisms for rapid exploration and comparison of multiple alternative mappings. We present a conceptual structure for interactive mappings, a basic framework for generating mappings from more diverse types of user-specified constraints than are supported by supervised learning, and the new Gesture Mapper user interface for mapping exploration and comparison.