Performance Comparison of Voice Activity Detectors for Acoustic Beehive Monitoring
Mahsa Abdollahi, Nico Coallier, Pierre Giovenazzo, Tiago Henrique Falk · 2023
Honeybees have a significant impact on agriculture, and their ability to pollinate is crucial for the economic viability of farms. The decrease in honey bee populations in recent years, coupled with the laborious task of manually inspecting beehives, has led to a growing interest in the automated remote monitoring of beehives. Out of the different modalities used to monitor honeybee colonies, acoustics has demonstrated great versatility. It has been shown that beehive audio can be used to detect e.g., swarming, queen absence, and hive strength. Notwithstanding, there are numerous external and environmental factors, such as rain, wind, traffic noise, and the presence of beekeepers’ voices in the background, which can significantly degrade the recorded bee audio quality and beehive monitoring performance. In this paper, we investigate the potential of three voice activity detectors (i.e., short-time energy thresholding, WebRTC, and a recent method based on a convolutional recurrent deep neural network) in detecting human speech within a beehive audio recording. We evaluate the performance of each method on two different datasets, one publicly available and another collected in-house. Experimental results show the superiority of WebRTC in detecting speech within bee buzzing audio, achieving F1-scores of approximately 0.7 and 0.8 for each dataset.