Our site selection intentionally deals with different volumetric constraints in order to see how neural network will perform in the creation of acoustic forms in unusual or difficult spaces.
New York City
211 E 26th St
New York, NY
320 E North Water St
100 ft (L) x 49.5 ft (W) x 47.5 (H)
30.5 m (L) x 15 m (W) x 14.5 m (H)
233,665 cubic foot
6,616 cubic meter
328 ft (L) x 147.6 ft (W) x 98.4 (H)
68.6 m (L) x 45 m (W) x 30 m (H)
3,272,080 cubic foot
92,655 cubic meter
Input for Graph Convolutional Neural Network
You will need the following three inputs to generate the outcome concert hall. Here is how you do it.
44 selected result models from the Graph CNN. 22 of which are generated from the small site, and the other half from the large site. We vary the input mesh shape, mesh counts, acoustic parameters, etc.
44 acoustic simulation results from Ecotect. The performance of sound waves is visualized through simulated particles. A range of results can be seen demonstrating how sound travels within our forms.
14 influencing factor for our Graph CNN. This diagram shows different influences and experiment results.
Section Style Transfer
Section drawings generated through another neural network: Style Transfer. Using existing concert hall section drawings to project an additional layer of information onto sections drawn from 4 selected models generated by our Graph CNN.
Selected examples of acoustic simulation.