## 1 Introduction  
Many beekeepers listen to their hives to ascertain the state of their honey bee colonies because bee buzzing carries information on colony behavior and phenology. Honey bees generate specific sounds when exposed to stressors such as failing queens, predatory mites, and airborne toxicants [1]. While experienced beekeepers can tell audio changes in sounds produced by stressed colonies, they may not always be able to determine the exact causes of the changes without hive inspections. Unfortunately, hive inspections disrupt the life cycle of bee colonies and put additional stress on the bees. The transportation costs are also a factor because many beekeepers drive long distances to their far flung apiaries due to increasing urban and suburban sprawls. Since beekeepers cannot monitor their hives continuously due to obvious problems with logistics and fatigue, a consensus is emerging among researchers and practitioners that electronic beehive monitoring (EBM) can help extract critical information on colony behavior and phenology without invasive beehive inspections and transportation costs [2]. When viewed as a branch of ecoacoustics [3], an emerging interdisciplinary science that investigates natural and anthropogenic sounds and their relations with the environment, audio beehive monitoring contributes to our understanding of how beehive sounds reflect the states of beehives and the environment around them.  
In this article, we contribute to the body of research on audio beehive monitoring by comparing several deep learning (DL) and standard machine learning (ML) methods in classifying audio samples from microphones deployed approximately 10 cm above Langstroth beehives' landing pads. In our experiments, the convolutional neural networks (ConvNets) performed on par with or better than the four standard ML methods on two curated datasets. On the first dataset of 10,260 audio samples (BUZZ1) where the training and testing samples were separated from the validation samples by beehive and location, a shallower raw audio ConvNet with a custom layer outperformed three deeper raw audio ConvNets without custom layers and performed on par with the four ML methods trained to classify feature vectors extracted from raw audio samples. On the second, more challenging dataset of 12,914 audio samples (BUZZ2) where the training and testing samples were separated from the validation samples by beehive, location, time, and bee race, all raw audio convolutional neural networks performed better than the four ML methods and a ConvNet trained to classify spectrogram images of raw audio samples obtained through Fourier analysis [4; 5].  
Our investigation gives an affirmative answer to the question of whether ConvNets can be used in real electronic beehive monitors that use low voltage devices such as Raspberry Pi [6] or Arduino [7]. Toward this end, we demonstrate that trained ConvNets can operate in situ on a credit card size Raspberry Pi computer. The presented methods do not depend on the exact positioning of microphones, and can be applied to any audio samples obtained from microphones placed inside or outside beehives provided that microphone propolization is controlled for. Our objective is to develop open source software tools for researchers, practitioners, and citizen scientists who want to capture and classify their audio beehive samples. To ensure the replicability of our findings and to establish performance benchmarks for interested research and citizen science communities, we have made public our two curated datasets, BUZZ1 (10,260 samples) and BUZZ2 (12,914 samples), of manually labeled audio samples (bee buzzing, cricket chirping, and ambient noise) used in this investigation [8].