| id |
acadia21_170 |
| authors |
Xydis, Achilleas; Perraudin, Nathanaël; Rust, Romana; Lytle, Beverly Ann; Gramazio, Fabio; Kohler, Matthias |
| year |
2021 |
| title |
Data-Driven Acoustic Design of Diffuse Soundfields |
| source |
ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 170-181. |
| doi |
https://doi.org/10.52842/conf.acadia.2021.170
|
| summary |
The paper demonstrates a novel approach to performance-driven acoustic design of architectural diffusive surfaces. It uses unsupervised machine learning techniques to analyze and explore the GIR Dataset, an extensive collection of real impulse responses and acoustically diffusive surfaces. The presented approach enables designers to explore many alternative acoustically-informed material patterns with various diffusive properties without requiring expert knowledge in acoustics. The paper introduces the computational pipeline, describes the used methods, and presents two use-cases in the form of design experiments. Finally, the paper discusses the challenges of developing such a method, its advantages, limitations, and future work. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (9,432,727 bytes) |
| references |
Content-type: text/plain
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 GIR Dataset: A Geometry and Real Impulse Response Dataset [Data set]
, Zenodo
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| last changed |
2023/10/22 12:06 |
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