Traffic Noise

Traffic noise research focuses on accurately mapping and modeling urban sound environments to mitigate noise pollution and improve urban planning. Current efforts leverage machine learning, particularly generative adversarial networks (GANs) and deep learning models, to create dynamic noise maps from limited data, improving accuracy and incorporating diverse sound sources beyond just traffic. These advancements enable more efficient noise assessments, aiding in urban design and potentially reducing noise-related complaints and improving soundscape quality for residents. The development of synthetic data generation techniques further enhances model training and accuracy, particularly for acoustic vehicle counting applications.