URBAN SONIFICATION: A Computational Design Framework of an Adaptive Sound Barrier System for Noise Reduction in Urban Context

 In an era of rapid technological and economic development, urbanization has exacerbated  noise pollution, posing collective human comfort and well-being challenges. Current urban  sound barriers are often static and limited in their effectiveness across different urban  settings and noise levels. There is a lack of integration between sound management and  adaptive technologies that can respond in real-time to changing urban soundscapes. This  thesis delves into developing an adaptive framework for urban sound barriers to reduce noise  pollution. By doing so, it seeks to mitigate the physical and mental health effects of excessive noise while fostering positive social impacts by establishing sound visualization  “boundaries” within urban spatial environments.  The experiment outlined in this thesis adopts a workflow that incorporates live sound  collection, data processing, machine learning, real-time sound visualization, acoustics  simulation with analysis, and prototyping. The primary objective is to develop a framework  for adaptive sound barrier systems tailored to different decibels and sound frequency levels.  These systems are assessed for their effectiveness in noise reduction through simulated  evaluation. Key methodologies include field acoustics simulation, as well as the integration  of sound visualization, sonification, the Internet of Things (IoT), tiny machine learning,  and design systemization.  Preliminary acoustics simulations indicated that adaptive sound barriers can effectively  reduce urban noise pollution. The systems demonstrate variability in response to different  noise levels and frequencies, showcasing the potential for broader application in diverse  urban settings. The findings suggest that adaptive sound barriers could revolutionize urban  planning and design, offering a dynamic solution to the persistent problem of noise pollution.  Further research will be needed to optimize system designs and evaluate long-term impacts  on urban environments. Future studies will explore materiality, scalability, and integration  with urban infrastructure.