# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 2 Related Work  
In respect to swarming early detection, many researchers have been monitoring physical variables to determine the status of the colony to help the beekeepers prevent colony losses. Studies have shown that sound analysis using Mel-Frequency Cepstral Coefficients (MFCC), by providing a spectral envelope that carries the identity of sound, can provide the appropriate information to deduce whether the colony is healthy or not [9]. Nevertheless, for apiary conditions, discrimination between glottal pulses and vocal tracts, as performed in human speech recognition, fails to obtain a pure envelope for bees and cannot offer significant results for early detection. Sensors monitoring and sampling beehive colony sound in the range of 120-2000 Hz can successfully detect harmful swarming events early. In addition to informing the beekeepers about the swarming event, much valuable and interesting information can be extracted by combining Fast Fourier Transformation (FFT) or Melody (Mel) sound analysis with temperature measurements. This allows the pinpointing of the time points close to the swarming event, providing differentiation between late detection and real-time occurrence. For example, bees tend to regulate the temperature inside the hive to around 35 degrees, but during swarming, the temperature varies from 17 to 36 degrees (constituting real-time detection) [10]. Several experiments have been reported in the literature, mainly in order to compare the beehive sound analysis outputs of Short-Time Fourier Transform (STFT) with MFCC [11]. Moreover, many algorithms, such as k-NN, SVM, and Hilbert Huang Transform (HTT), have been utilized to underline the difference between situations inside the colony during swarming [12, 13]. Research has shown that during a swarming event, the bees' sound frequency content shifts from 125-300 Hz to 400-500 Hz, because of the intense flitting of the bees' wings, which also causes a temperature reduction (2-3 degrees) [14]. In addition, many experiments show the influence of multiple harmful-to-the-colony events on frequency bands, as shown in Table 1.  
As Table 1 shows, differences among the detected frequencies in similar events (e.g., during the day) are obvious since the waggling of bees inside a colony depends on various factors, such as the time of the day, the season, and the colony location. However, there is a precise match in the frequency ranges observed during the swarming or waggle dance. This allows us to derive conclusions about the colony's behavior during those events, detect them early, and act accordingly. To continuously observe and record the behavior of the bees during stressful events, different monitoring systems have been created that send the data to the cloud from IoT devices, implemented inside and outside the beehive [21, 22, 23]. As mentioned above, one of the most developed and precise ways to detect harmful events is the procedure of sound processing. Many studies have shown that in the apiary, the MFCC and Linear Prediction Coefficients (LPC) feature extraction techniques provide the optimal results to maximize the accuracy of the results when the proposed model is properly trained [24, 25]. [PERSON] has also noted that systems that detect destructive colony events, such as swarming, prevent the event and provide useful information, such as the season and the time that those events tend to happen [14]. In this way, system managers and beekeepers can detect any referred events early, which will finally lead to infinitesimal losses. Observing a colony's behavior is not limited to sound analysis, as image processing techniques can also provide useful early detection information. For example, [PERSON] presented a CNN algorithmic process to detect early swarming using a camera placed inside a beehive frame and utilizing the Faster-Region-Based Convolutional Neural Network (R-CNN) and Single Shot Detection (SSD) algorithms [26]. That system can offer accurate late swarming detection. However, its early detection capabilities are rather inaccurate.