MFCC-based descriptor for bee queen presence detection

Abstract

Monitoring and detecting hive health are essential for the development of ecosystem conservation strategies and sustainable beekeeping. The absence of the queen bee is a warning signal and indicates an anomalous situation. The literature contains promising machine learning and deep learning techniques for detecting the presence of the queen bee through the spectral temporal analysis of hive sounds. Despite works that consider handcrafted and deep features to describe the problem, there is a lack of feature selection to evaluate which descriptors could better discriminate the presence/absence of queen bee. Additionally, deep learning techniques lead to high-dimension descriptors and a high computational cost. In this context, we explore feature extraction and selection techniques to obtain efficient and compact descriptors that can perform classification in a real-time monitoring scenario. The results reveal that combining cepstral, time, and frequency features achieve 0.99 for accuracy, kappa, area under curve, specificity, and sensibility metrics, outperforming most state-of-the-art models for queen bee presence classification, including convolution neural network models. The feature selection step notably reduces the descriptor size and maintains classification performance. The 15 and 31 mel-frequency cepstrum coefficients descriptors have a low dimension and are based on only one feature, which reduces the number of calculation and library dependencies. Given this, we believe that our findings can support deployment in low-computational devices for non-intrusive and real-time hive health diagnoses.