This paper presents the analysis and development of a smart noise information collection system utilizing the Spectrum Analyzer SVAN 958A to enhance real-time noise monitoring and data analysis. The study addresses the limitations of traditional noise measurement tools, which often lack real-time processing and comprehensive integration with modern data management platforms. By leveraging the SVAN 958A’s advanced frequency domain analysis capabilities and integrating it with IoT-based technologies and machine learning algorithms, the proposed system aims to improve noise data accuracy, automation, and scalability. Through the design and implementation of a real-time data processing framework, the system enables precise noise source identification and facilitates immediate response to noise pollution issues. The system’s performance was validated in various urban and industrial noise environments, demonstrating significant improvements in noise data collection accuracy, with enhanced signal differentiation by up to 35% over traditional methods. Additionally, real-time data visualization tools were developed to support regulatory compliance and decision-making processes. The results of this research suggest that the proposed smart noise information collection system can serve as an efficient tool for environmental noise monitoring, offering both practical benefits for public health and potential applications in smart city infrastructure. The system also opens avenues for further research in integrating advanced analytics into acoustic monitoring frameworks, contributing to the ongoing development of smart environmental management technologies.
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