A data acquisition setup for data driven acoustic design

TL;DR

This paper introduces an automated data acquisition system for studying how diffusive surface structures affect acoustics, combining computational design, 3D printing, robotic measurement, and machine learning prediction.

Contribution

It presents a novel interdisciplinary setup integrating computational design, robotic measurement, and machine learning for acoustic surface analysis.

Findings

Automated robotic process effectively measures impulse responses.

Generated data enables prediction of acoustic responses from surface geometries.

Initial comparative studies show promising results for surface acoustics analysis.

Abstract

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.