## Materials and methods
### Audio Data
We 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 of us could hear bee buzzing. The C category consisted of the audio files captured at night where at least two of us could hear the chirping of crickets and no bee buzzing. The N category included all samples where none of us could clearly hear either bee buzzing or cricket chirping. These were the samples with sounds of static microphone noise, thunder, wind, rain, vehicles, human conversation, sprinklers, and relative silence, i.e., absence of any sounds discernible to a human ear. We chose these categories for the following reasons. The B category is critical in profiling acoustic signatures of honeybee colonies to identify various colony stressors. The C category is fundamental in using audio classification as a logical clock because cricket chirping is cyclical, and, in Northern Utah, is present only from 11:00 p.m. until 6:00 a.m. The N category helps EBM systems to identify uniquely individual beehives because ambient noise varies greatly from location to location and from beehive to beehive at the same location. The first labeled dataset, which we designated to be our train/test dataset, included 9110 samples from beehives 1.1 and 2.1: 3000 B samples, 3000 C samples, and 3110 N samples. We later labeled another dataset, which we designated to be our validation dataset, of 1150 audio samples (300 B samples, 350 N samples, and 500 C samples) captured from beehives 1.2 and 2.2 in May and June 2017. Thus, the audio samples in the validation dataset were separated from the audio samples in the training and testing dataset by beehive and location. In summary, our first curated dataset, which we called BUZZ1 [8], consisted of 10,260 audio samples: a train/test dataset of 9110 samples that we used in our train/test experiments and a validation dataset of 1150 audio samples that we used in our validation experiments for model selection. In BUZZ1, the degree of data overlap between the training and testing datasets in terms of the exact beehives being used is controlled for by the fact that audio samples from different beehives differ from each other even when the hives are located in the same apiary. To better control for data overlap, we created another curated dataset, BUZZ2 [8], of 12,914 audio samples where we completely isolated the training data from the testing data in terms of beehive and location by taking 7582 labeled samples (76.4%) for training from beehive 1.1 in apiary 1 and 2332 labeled samples (23.52%) samples for testing from beehive 2.1 in apiary 2. The training set of 7582 samples consisted of 2402 B samples, 3000 C samples, and 2180 N samples. The testing set of 2332 samples consisted of 898 B samples, 500 C samples, and 934 N samples. The validation dataset of BUZZ2 used for model selection was obtained from two Langstroth beehives (1.3 and 1.4) in apiary 1 obtained in May 2018. These beehives did not exist in apiary 1 in 2017. Both were freshly initiated with Carnional honeybee colonies and started being monitored in May 2018. Thus, in BUZZ2, the train/test beehives were separated by beehive and location while the validation beehives were separated from the train/test beehives by beehive, location, time (2017 vs. 2018), and bee race (Italian vs. Carnional). The validation dataset included 1000 B samples, 1000 C samples, and 1000 N samples.