Noise pollution is a significant environmental and social problem for rail transport and urban areas. This paper describes an approach to noise monitoring based on the integration of artificial intelligence (AI) and machine learning (ML) into acoustic data collection and analysis systems. The SVAN 958A spectral analyzer was used as the measuring equipment, which allows obtaining accurate noise data in real time. ML algorithms are used for automatic noise detection, in particular, tram noise, in order to improve the quality of classification and analysis. For data visualization and results management, interactive dashboards were created in the Grafana environment, which are integrated into the overall smart city management system. These dashboards provide the opportunity to monitor noise pollution in real time, predict its level and make operational decisions to reduce the impact of noise on the urban environment. The proposed system demonstrates practical effectiveness due to the combination of data collection tools, machine learning methods and a user-friendly visualization interface. Its implementation allows to improve the quality of noise pollution monitoring, contribute to reducing noise levels and improve the environmental situation, ensuring comfortable living conditions in the urban environment.
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