Design and Fabrication of Acoustic Sensor with Application of Environment Monitoring.

Abstract - Environmental acoustic monitoring requires reliable acquisition and processing of continuously varying acoustic signals to distinguish different acoustic conditions in practical environments. This paper presents the design, implementation, and experimental validation of an integrated acoustic monitoring system combining a microphone-based sensing interface, LM358-based analog signal conditioning, Arduino-based digital acquisition, and RMS-based digital signal processing. The developed sensing module incorporates microphone biasing, AC coupling, reference-voltage generation, signal conditioning, and ADC interfacing to obtain a suitable digital representation of the acquired acoustic signal. The implemented hardware was experimentally characterized using controlled acoustic inputs and laboratory measurement instruments to verify its sensing and signal-conditioning performance, followed by practical evaluation in a library environment. The acquired acoustic samples were subjected to DC-offset removal and root mean square (RMS) analysis to obtain an amplitude-domain representation of acoustic activity. An experimentally collected dataset was further analyzed using Python-based data processing to establish data-driven RMS boundaries for categorizing acoustic conditions. The resulting classification criteria were incorporated into the Arduino-based real-time processing framework, enabling continuous acoustic condition identification and local visualization through an LCD interface. The system was additionally integrated with a cloud-based data logging framework for continuous monitoring and visualization. The developed prototype demonstrates a complete engineering workflow from acoustic signal acquisition and analog front-end implementation to digital signal processing, experimental data analysis, data-driven classification, and real-time environmental monitoring, providing a practical platform for acoustic assessment in noise-sensitive environments.