Summary of A Rapid Approach to Urban Traffic Noise Mapping with a Generative Adversarial Network, by Xinhao Yang et al.
A rapid approach to urban traffic noise mapping with a generative adversarial network
by Xinhao Yang, Zhen Han, Xiaodong Lu, Yuan Zhang
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applied Physics (physics.app-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel generative adversarial network (GAN)-based approach for rapid urban traffic noise mapping is proposed, leveraging urban elements such as roads and buildings as inputs. This technique enables the assessment of urban traffic noise distribution and facilitates integration into Grasshopper, allowing non-acoustics experts to predict acoustic impacts in early design stages. The model achieves mean squared error (RMSE) and structural similarity index (SSIM) values of 0.3024 dB(A) and 0.8528, respectively, for the validation dataset. This approach addresses limitations of traditional grid noise mapping methods, including time consumption, software costs, and lack of parameter integration interfaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to map traffic noise in cities is being developed. It uses special computer models that can quickly calculate how loud different parts of a city are. These models use information about roads, buildings, and other urban features as inputs. This helps urban designers and planners predict what will happen if they change the design of a street or building, without needing to be experts in noise pollution. |
Keywords
» Artificial intelligence » Gan » Generative adversarial network
