<![CDATA[This study presents a functional prototype of an Android-based classroom noise-mapping system that uses smartphone microphones, Zero Crossing Rate (ZCR), and Firebase Realtime Database. Two integrated applications were developed: a student-side sensing client and a lecturer monitoring dashboard. Functional testing covered three source conditions (human speech, a dropped pen, and table movement) at three source desks. Each event was observed at seven desk positions, producing 63 sensor-location records from nine source events. The active source position was detected in all nine events. Source-classification accuracy was 77.8% (7/9): human speech and dropped-pen events were correctly classified in all trials, while only one of three table-movement events was classified as an object and two were returned as unclear. The results demonstrate real-time synchronization and functional spatial monitoring, while also showing sensitivity to sound propagation and overlapping acoustic patterns. Because the documented prototype uses an uncalibrated level indicator and its appendix retains a simulated audio-buffer routine, the findings should be interpreted as functional prototype validation rather than calibrated sound-level or field-performance validation.]]>
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