International Journal of Science, Engineering and TechnologyAuthors: Aniritha S, Rithanya S, Ajissa Jasmin, Dr. Saravanakumar R
Abstract: Environmental noise monitoring systems commonly focus on sound-level measurement, while source-aware interpretation and adaptive detection are often treated as separate functions. This work presents a low-cost LabVIEW-based framework for real-time environmental noise monitoring and source classification. An electret microphone and LM358-based signal-conditioning stage acquire the acoustic signal, while an Arduino Nano provides analog-to-digital conversion and serial communication with LabVIEW. The software computes the root-mean-square (RMS) magnitude, calibrated sound level, fast Fourier transform (FFT), equivalent continuous sound level (Leq), maximum sound level (Lmax), and duration of threshold violation. Instead of using only a fixed alarm level, the proposed monitoring strategy estimates the local background-noise condition and adapts the detection threshold to reduce unnecessary alerts. Frequency-domain features are then used to distinguish representative environmental sources such as traffic/engine noise, horns, speech, music, and construction noise. A LabVIEW dashboard provides real-time visualization, alerts and data logging. The hardware prototype has been integrated and the Arduino-LabVIEW interface and experimental validation are being completed. The final evaluation will compare the prototype against a reference sound-level meter and quantify measurement error and classification performance
DOI: https://doi.org/10.5281/zenodo.23570976
