# Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
## 2 Audio Datasets  
We obtained our first audio datasets from six BeePi monitors deployed in Logan, UT and North Logan, UT on Langstroth hives with Carnional and Italian honeybee colonies in 2017-2018 [8]. We placed the microphones \(\approx\)10 cm above the hives' landing pads (See Figure 2). Each monitor saved a 30-s audio wav file every 15 min on a USB storage device connected to the monitor's pi computer. Each 30-s audio sample was automatically segmented into 2-s wav samples with a 1-s overlap, which resulted in 28 2-s wav samples per one 30-s audio file. We obtained the ground truth by manually labeling 2-s audio samples. Three human listeners listened to each sample and placed it into one of the three non-overlapping categories: bee buzzing (B), cricket chirping (C), and ambient noise (N). The B category consisted of the samples where at least two listeners heard bee buzzing. The C category consisted of the audio files collected at night where at least two listeners heard the chirping of crickets and no bee buzzing. The N category included all samples where none of the human listeners could clearly hear either bee buzzing or cricket chirping. The N category included samples with static microphone noise, thunder, wind, rain, vehicles, human  
Figure 1: BeePi monitor (**a**) with a solar panel on the side; two BeePi monitors with solar panels behind the hives. conversation, sprinklers, and other types of ambient noise. We used the same curation techniques on all audio datasets described in this article. We called the first labeled dataset BUZZ1. As shown in Table 1, this dataset includes 10,260 audio samples: 6494 training samples (63%), 2616 training samples (25%), and 1150 validation samples (12%) used for model selection. The samples in the validation dataset are separated from the audio samples in the training and testing samples by beehive and location. Several months later we curated another dataset, which we called BUZZ2, of 12,914 audio samples by taking 7582 (76.4%) labeled samples for training from a beehive in one apiary and 2332 (23.52%) labeled samples for testing from a different beehive in a different apiary. The sample distribution of BUZZ2 is given in Table 2. All training and testing data were obtained from Italian honeybee colonies in 2017 whereas the validation data for model selection were obtained from two Carniolan colonies in 2018. Thus, in BUZZ2, the train/test samples are separated by beehive and location while the validation beehives are separated from the train/test beehives by beehive, location, time (2017 vs. 2018), and bee race (Italian vs. Carniolan). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Bee (B)** & **Cricket (C)** & **Noise (N)** & **Total** \\ \hline Training & 2128 & 2128 & 2238 & 6494 \\ \hline Testing & 872 & 872 & 872 & 2616 \\ \hline Validation & 300 & 500 & 350 & 1150 \\ \hline Total & 3300 & 3500 & 3460 & 10,260 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Audio sample distribution in BUZZ1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Bee (B)** & **Cricket (C)** & **Noise (N)** & **Total** \\ \hline Training & 2402 & 3000 & 2180 & 7582 \\ \hline Testing & 898 & 500 & 934 & 2332 \\ \hline Validation & 1000 & 1000 & 1000 & 3000 \\ \hline Total & 4300 & 4500 & 4114 & 12,914 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Audio sample distribution in BUZZ2. Figure 2: Microphones placed \(\approx\)10 cm above landing pads in Beepi monitors in North Logan, UT (**a**) and Logan, UT (**b**). We used BUZZ1 and BUZZ2 in our raw audio classification experiments with convolutional neural networks (ConvNets) and four standard ML methods: logistic regression (LR), k-nearest neighbors (KNN), support vector machines (SVM), and random forests (RF). All ConvNets were trained, tested, and validated on raw audio data (i.e., amplitude vectors) without any feature extraction.