  In this paper, we present a novel interdisciplinary approach to study the
relationship between diffusive surface structures and their acoustic
performance. Using computational design, surface structures are iteratively
generated and 3D printed at 1:10 model scale. They originate from different
fabrication typologies and are designed to have acoustic diffusion and
absorption effects. An automated robotic process measures the impulse responses
of these surfaces by positioning a microphone and a speaker at multiple
locations. The collected data serves two purposes: first, as an exploratory
catalogue of different spatio-temporal-acoustic scenarios and second, as data
set for predicting the acoustic response of digitally designed surface
geometries using machine learning. In this paper, we present the automated data
acquisition setup, the data processing and the computational generation of
diffusive surface structures. We describe first results of comparative studies
of measured surface panels and conclude with steps of future research.
