Authors - Mark Alfred M. Nuguit, Elleicarjay C. Ramilo, Noel B. Linsangan Abstract - Urban noise events in busy metropolitan areas present significant challenges to urban planning and environmental management. This study introduces a cost-effective system integrating wireless sensor networks with deep learning to capture and classify urban noise events in Metro Manila. The prototype employs sensor nodes equipped with ESP32 microcontrollers and MAX9814 microphone amplifiers to record audio data, which is stored on SD cards and subsequently converted into 512×512 pixel spectrogram images using Python-based signal processing. These images serve as inputs to a MobileNet-based convolutional neural network, fine-tuned via transfer learning on a dataset of over 4,300 samples spanning two categories: civilian vehicles and human activities. The system, implemented on a Raspberry Pi with an interactive touchscreen interface, achieved an overall classification accuracy of 96.11%, as verified through confusion matrix analysis. This work demonstrates a scalable, low-cost framework for urban noise monitoring and provides valuable insights for environmental management and future urban planning strategies. The method’s efficiency and adaptability make it especially suitable for addressing the unique acoustic challenges of rapidly urbanizing regions.
