• Devang Katekhaye
• Sonali Chanekar
• Payal Kailakhe
• Shreya Awale
• Vinay Wagh

Rapid urban growth has significantly increased environmental noise, creating serious concerns for public health and urban sustainability. Conventional monitoring systems often lack real-time capabilities and fail to capture dynamic variations in city soundscapes. This paper proposes an AI-driven noise pollution mapping and analysis system designed for modern urban environments. The system integrates IoT-based acoustic sensors with advanced signal processing and machine learning techniques to capture, analyze, and classify noise sources such as traffic, construction, and human activity. It generates real-time, location-aware noise maps that highlight spatial distribution and temporal variations in noise intensity. Additionally, predictive modeling is used to forecast future noise trends, enabling proactive urban planning and effective noise management. The proposed framework is scalable, cost-efficient, and demonstrates improved accuracy compared to traditional methods. Overall, it provides a smart and data-driven solution for monitoring and reducing urban noise pollution.

AI -Based, Noise pollution, IOT Based

"Ai Powered Noise Pollution Mapper and Analyzer for Urban Spaces", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.f360-f363, March-2026, Available :http://www.jetir.org/papers/JETIR2603547.pdf

"Ai Powered Noise Pollution Mapper and Analyzer for Urban Spaces", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppf360-f363, March-2026, Available at : http://www.jetir.org/papers/JETIR2603547.pdf