Predicting City Soundscape for Designers using Neural Network in Rhino Grasshopper and c#

Описание: #rhino #grasshopper #csharp #neuralnetwork

Machine Learning Project: Predicting city soundscape using a Nural Network

Every city, neighborhood, block, and the building has its own soundscape and mixture of tones. If this soundscape is perceived as noisy or pleasant is a subjective matter to be decided by every individual themselves. Most of the time architects don’t specifically design sound aware, only when a client specifically asks for sound-aware design considerations. In that case, a sound consultant has to be brought into the team, to give insight into how noise can be suppressed or nurtured, how much noise is bearable and what can be done with geometry and architectural building mass. Simply, architects do not have the right tools to get real-time feedback on the impact of their design on the soundscape surrounding it. Sound samples have been captured with professional hardware to register the sound pressure at specific locations, which have been related to distances to geometrical conditions, traffic frequency of streets and train tracks as well as natural elements like trees, which might be home to birds and wildlife. A neural network has been utilized and trained using a set of hyperparameters consisting of adjacencies and distances, which could then be used to predict the soundscape for a fictive new development, fed back to the designers in real time.