Review on Iot, AI and Ml Based Noise Source Identification, Monitoring and Controlling Device With Alert Mechanism

Recently, more and more issues associated with urban noise pollution have been addressed. That is why recent urban noise pollution appears as key problem causing millions worldwide to sleep poorly, develop cardiovascular issues, and live a welfare lower-quality life. Conventional means of monitoring sound are oriented towards inspection of noise and do not have sound analysis intelligence to act on its measure. This article discusses how an artificial intelligence and machine learning, IoT based noise source isolation system works which includes continuous monitoring, and the necessary automation mechanism of control. This solution assumes the integration of sensors on one side, while state-of-the art edge computing approach incorporating trained machine learning models works on the other hand. After noise sources have been localized, the systems then go on to hold the noise levels within the regulators limits and send out alerts whenever the regulations have not been adhered to within the required duration. In this context, Acoustic signals are passed through a Convolutional Neural Network in a forward model. This network is able to discern different sources of noise such as human activity, traffic, construction, and equipment, with very close accuracy. Furthermore, data consolidation, predictive analysis, and user alerting are executed through IoT cloud platform delivered through mobile and web dashboards. The Study investigates the application of artificial intelligence (AI) and machine learning (ML) in determining the sources of noise with superior algorithms for the analysis of acoustic data, thereby being able to analyses more efficiently and accurately. A system employing both supervised and unsupervised learning approach can indicate the main sources, classify noise pattern, and develop actionable knowledge of their origins. Major innovations in this work include the introduction of convolutional neural networks for sound feature extraction, clustering techniques for source localization, and real-time processing capabilities in dynamic environments