Bees play an important role in agriculture and ecology, and their pollination efficiency is essential to the economic profitability of farms. The drastic decrease in bee populations witnessed over the last decade has attracted great attention to automated remote beehive monitoring research, with beehive acoustics analysis emerging as a prominent field. In this paper, we review the existing literature on bee acoustics analysis and report on the articles published between January 2012 and December 2021. Five categories are explored in further detail, including the origin of the articles, their study goal, experimental setup, audio analysis methodology, and reproducibility. Highlights and limitations in each of these categories are presented and discussed. We conclude with a set of recommendations for future studies, with suggestions ranging from bee species characterization, to recording and testing setup descriptions, to making data and codes available to help advance this new multidisciplinary field.

The vital role of honeybees in pollination and their high rate of mortality in the last decade have raised concern among beekeepers and researchers alike. As such, robust and remote sensing of beehives has emerged as a potential tool to help monitor the health of honeybees. Over the last decade, several monitoring systems have been proposed, including those based on in-hive acoustics. Despite its popularity, existing audio-based systems do not take context into account (e.g., environmental noise factors), and thus the performance may be severely hampered when deployed. In this paper, we investigate the effect that three different environmental noise factors (i.e., nearby train rail squealing, beekeeper speech, and rain noise) can have on three acoustic features (i.e., spectrogram, mel frequency cepstral coefficients, and discrete wavelet coefficients) used in existing automated beehive monitoring systems. To this end, audio data were collected continuously over a period of three months (August, September, and October) in 2021 from 11 urban beehives located in downtown Montréal, Québec, Canada. A system based on these features and a convolutional neural network was developed to predict beehive strength, an indicator of the size of the colony. Results show the negative impact that environmental factors can have across all tested features, resulting in an increase of up to 355% in mean absolute prediction error when heavy rain was present.

Self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, IoT-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, non-invasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation.

Honeybees (Apis mellifera) are vital to agriculture and biodiversity, serving as primary pollinators for numerous crops and wild plants. However, the decline in bee populations due to factors such as pesticides, pathogens, parasites, and climate variations poses a serious threat to food security and ecological balance. This study introduces the use of modulation spectral features, extracted from beehive acoustic signals, to better predict the strength of the honeybee colony. Experiments conducted on the public urban beehive acoustics and phenotyping dataset (UrBAN), comprised of over 3000 h of beehive audio recordings, show the proposed features offering improved predictive performance compared with traditional audio features. This work underscores the potential of automated, noninvasive acoustic monitoring systems to support sustainable beekeeping and ecological preservation.

Honey bees play a vital role in global ecosystems and agriculture through their pollination services. However, the Varroa destructor mite remains one of the most damaging parasites affecting honeybee health, contributing significantly to colony decline. Early detection of Varroa infestation is essential for effective beekeeping management. In this study, we propose a machine learning approach for predicting Varroa mite infestation levels based on acoustic analysis of hive recordings. We introduce a set of nine spectral shape descriptors (SSD) computed from conventional and modulation spectrograms to characterize the acoustic environment within hives. We evaluate the predictive power of these features against traditional cepstral features across multiple classifiers, including Support Vector Machines, Random Forests, and k-Nearest Neighbors. Our results demonstrate that the proposed SSDs significantly improve classification performance relative to conventional features across numerous figures-of-merit, particularly in hive-independent test settings. This work highlights the potential of spectral acoustic monitoring combined with supervised learning as a scalable and non-invasive tool for precision beekeeping and early detection of parasitic threats.

Honeybees (Apis mellifera) are vital to agriculture and biodiversity, serving as primary pollinators for numerous crops and wild plants. However, the decline in bee populations due to factors such as pesticides, pathogens, parasites, and climate variations poses a serious threat to food security and ecological balance. This study introduces the use of modulation spectral features, extracted from beehive acoustic signals, to better predict the strength of the honeybee colony. Experiments conducted on the public UrBAN dataset, comprised of over 3,000 hours of beehive audio recordings, show the proposed features offering improved predictive performance compared to traditional audio features. This work underscores the potential of automated, non-invasive acoustic monitoring systems to support sustainable beekeeping and ecological preservation.