## 2 Background  
As an integral component of EBM, audio beehive monitoring has attracted considerable research and development effort. [PERSON] et al. [1] designed a system for profiling acoustic signatures of honeybee colonies, analyzing these signatures, and identifying various stressors for the colonies from this analysis. A foam insulated microphone probe assembly is inserted through a hole drilled in the back of the upper super of a Langstroth hive that consists of two supers. The hole is drilled so that the probe is inserted between honeycomb frames at least four inches down from the top of the upper super, because brood frames are typically located in the two lower supers of a standard Langstroth hive. The probe's microphone is connected via a microphone amplifier to a computer with an audiocard and the audio analyzing software that performs the Fast Fourier Transform (FFT) of captured wav files and then saves the processed data to text files for subsequent analysis. The frequency spectrum is processed into a running average and an overall average. The two averages are exported to text-based files and are analyzed with statistical software to associate sound spectra with acoustic variations. In an experiment, bee colonies treated with naptha, ammonia, or toluene all produced statistically different scores with each stressor producing a unique acoustic signature. The researchers state that these associations can be detected with artificial neural networks (ANNs) but offer no experimental evidence of this claim. [PERSON] et al. [9] designed a system for monitoring swarm sounds in beehives. The system consisted of a microphone, a temperature sensor, and a humidity sensor placed in a beehive and connected to a computer in a nearby barn via underground cables. The sounds were recorded at a sample rate of 2 kHz and analyzed with Matlab. The researchers monitored three beehives for 270 h and observed that swarming was indicated by an increase of the buzzing frequency at about 110 Hz with a peak at 300 Hz when the swarm left the hive. Another finding was that a swarming period correlated with a rise in temperature from 33\({}^{\circ}\) C to 35\({}^{\circ}\) C with a temperature drop to 32\({}^{\circ}\) C at the actual time of swarming. [PERSON] et al. [10] presented a method to analyze and identify honey bee swarming events through a computational analysis of acoustic vibrations captured with accelerometers placed on the outside walls of hives. The researchers placed two accelerometers on the outer walls of two Langstroth hives with Apis mellifera honey bees, one accelerometer per hive. A cavity was drilled in the center of the back wall of each hive. Both hives were located in close proximity to each other and approximately 10 m away from a house with a PC running a Linux distribution. The accelerometers were placed in the cavities and connected to a dual channel conditioner placed between the hives and encased in a waterproof acrylic box. The conditioner was coupled to the indoor PC with two coaxial cables through which the captured vibration samples were saved on the PC's hard disk. An ad hoc roof was placed above both hives to control for vibrations caused by rain drops. The vibration samples, each one hour long, were logged from April to June 2009. Averaged frequency spectra were computed for a frequency resolution of 20 Hz and an averaging time of 510 s. The averaged spectra were combined into one day long spectrograms which were analyzed with the Principal Component Analysis (PCA) for feature extraction. The witnessed swarming events were juxtaposed with the corresponding spectrograms and were found to exhibit a unique set of features. A subsequent minimization computer analysis suggested that swarming may be detected several days in advance. Peaks in vibrational activities were discovered at 250 Hz, 500 Hz, 750 Hz, and 2000 Hz. [PERSON] and [PERSON] [11] developed a distributed audio monitoring system for apiaries.