Authors: Ran Yue, Qi Meng, Da Yang, Yue Wu, Fangfang Liu, Wei Yan

In this study, a model is constructed to predict several features of the acoustic environment in an urban design context, such as sound pressure level (SPL), sound source location, and soundscape quality. The model uses a grid-based method and a Gaussian mixture model (GMM) in a machine learning framework to consider urban design factors. 

To train the model, first data is collected. Subjective perceptual data is obtained on site using the soundwalk method in three sample urban parks. This data is then used to evaluate the model’s correctness. The model is trained to predict the relationship between design factors and the resulting acoustic environment. The trained model can predict soundscape quality, and displaying the prediction results by combining geographic, visual, and auditory data. 

The soundscape prediction is particularly relevant in the context of urban planning. The model’s output enables users to quickly compare the trade-off between different urban park design features and their impact on the resulting acoustic environment. The study also proposes specific optimization techniques for improving the soundscape quality in urban parks based on the model’s simulation findings. 

Software & Plug-Ins Used

• ArcGIS for grid division methods 

Paper Information

• Title: A visualized soundscape prediction model for design processes in urban parks 
• Author(s): Ran Yue, Qi Meng, Da Yang, Yue Wu, Fangfang Liu, Wei Yan 
• Year: 2022
• Link: https://link.springer.com/article/10.1007/s12273-022-0955-3
• Type: Journal Paper
• ML Tags: Gaussian Mixtures
• Topic Tags: Urban Planning, Acoustics