Bee Sound Acquisition in a Natural Environment

Abstract

The preservation of bee populations is essential for maintaining global ecological balance and agricultural productivity. Given that traditional hive monitoring techniques are invasive and labor-intensive, acoustic monitoring has emerged as a superior non-invasive alternative. This paper presents the development and field implementation of an autonomous system for bee sound acquisition and advanced signal characterization. The hardware architecture is based on a Raspberry Pi 4 platform integrated with high-precision micro-electro-mechanical systems (MEMS) microphones, specifically designed for continuous operation in natural, outdoor environments. The study details a robust digital signal processing pipeline, including multi-stage filtering, signal stabilization, and the extraction of critical acoustic features such as zero-crossing rate (ZCR), root mean square (RMS) energy, and mel-frequency cepstral coefficients (MFCC). By utilizing Mel-spectrograms and spectral analysis, the system provides a detailed time-frequency representation of the colony's acoustic signatures. The results demonstrate the effectiveness of the proposed acquisition protocol in isolating biological signals from environmental noise, establishing a reliable foundation for long-term acoustic surveillance and biodiversity monitoring in apiculture.