## 2 Background
The system consists of nodes and wireless sensor units with one node per apiary and one sensor unit per hive. A node is a solar-powered embedded computer. A sensor unit consists of an omnidirectional microphone and a temperature sensor. Sensor units are placed at the bottom of each hive protected by a grid to prevent propolization. The system was tested in an apiary of 15 Langstroth hives where up to 10 hives were monitored continuously by taking 8 s audio samples every hour with a sampling rate of 6250 Hz. The timestamped frequency spectra obtained with Fourier transform and temperature data were logged in a SQL database from May 2008 to April 2009. The researchers reported that sound volume and sound intensity at medium and low frequencies showed distinguishable daily patterns, especially in the spring. Sound volume did not have identifiable patterns in the winter. [PERSON] and [PERSON] [12] also investigated audio signals of honeybee swarms. Five custom designed observation hives were sealed with glass covers. The captured video and audio data were monitored daily by human observers. By manually analyzing the captured audio samples and correlating them with the corresponding video samples, the researchers found that approximately one hour before swarm exodus, the production of piping signals gradually increased and ultimately peaked at the start of the swarm departure. [PERSON] et al. [13] designed an algorithm for digitizing bee buzzing signals with harmonic intervals into A440 piano note sequences to obtain symbolic representations of buzzing signals over specific time periods. When viewed as a time series, such sequences correlate with other timestamped data such as estimates of bee traffic levels or temperatures [14; 15]. The note range detected by the proposed algorithm on 3421.52 MB of 30-s wav files contained the first four octaves, with the lowest note being A0 and the highest note being F#4. It was observed that the peaks in the frequency counts, as the selected 24-h time period progressed, started in the first octave (D1), shifted higher to C3 and C#3 in the third octave, and returned back to the first octave at the end of the selected time period. Several notes in the fourth octave, e.g., F#4 and C#4, were also detected, but their frequency counts were substantially lower than those of the peaks in the first three octaves. Representation learning is a branch of AI that investigates automatic acquisition of representations from raw signals for detection and classification [16]. Standard machine learning (ML) techniques are believed to be limited in their ability to acquire representations from raw data because they require considerable feature engineering to convert raw signals into feature vectors used in classification. Unlike conventional ML techniques, deep learning (DL) methods are believed to be better at acquiring multi-layer representations from raw data because real data often encode non-linear manifolds that low-order functions have a hard time modeling. In a DL architecture, each layer, starting from raw input, can be construed as a function transforming its input to the one acceptable for the next layer. Many features of these layers are learned automatically by a general purpose procedure known as _backpropagation_[17]. DL methods have been successfully applied to image classification [18; 19; 20], speech recognition and audio processing [21; 22], music classification and analysis [23; 24; 25], environmental sound classification [26], and bioinformatics [27; 28]. Convolutional neural networks (ConvNets) are a type of DL feedforward neural networks that have been shown in practice to better train and generalize than artificial neural networks (ANNs) on large quantities of digital data [29]. In ConvNets, filters of various sizes are convolved with a raw input signal to obtain a stack of filtered signals. This transformation is called a _convolution layer_. The size of a convolution layer is equal to the number of convolution filters. The convolved signals are typically normalized.