Threshold Detection-Based Intelligent Sensor for Bird Vocalization Recognition

To address ecological monitoring challenges in urban wetland ecosystems, this study designed and implemented an automated avian vocalization acquisition system utilizing field-collected avian acoustic data from representative wetland habitats in Beijing. Through comparative analysis of signal parameter variations between avian vocalizations and ambient noise, we formulated multiple threshold detection algorithms for avian sound identification and systematically evaluated the feasibility of various feature fusion approaches. The proposed system integrates automated audio recording, real-time avian presence detection, and autonomous data transmission functionalities. Experimental validation demonstrated 85% detection accuracy for target species' vocalizations under unattended operational conditions. This technological framework provides an innovative solution for biodiversity monitoring in urban wetlands, advancing methodological developments in soundscape ecology while offering pragmatic implications for conservation management strategies.