# MFCC Selection by LASSO for Honey Bee Classification
## 1 Introduction
### Sounds of Honey Bees and Swarming Prediction  
The \"buzzing\" sound generated during a bee flight is primarily produced by swift wing movement. A bee can produce other sounds by the coordinated vibration of various body parts, but this paper is focused on non-interfering monitoring outside of a hive, and thus we limit our approach to the buzzing sound produced by a flying bee. The rate of wing beats can approximately count even up to 260 beats per second [23]. The frequency of the buzzing sound can vary depending on the size and species of the bee. A steady hum around 180 Hz is a basic sound produced by stationary bees during breathing movements, while the flight sound has a frequency around 250 Hz [24]. [PERSON] claimed in [24] that for the audio signals registered inside a hive and cut to the frequency band of only 225-285 Hz, there was a distinguishable warble, present even 25 days before the issue of the swarm. Based on his discovery, he invented and patented the 'Apidictor' device to predict the swarming much earlier than any visible signs could be noticed by a beekeeper. The placement of a microphone inside a hive is a challenging task because bees recognize it as a foreign object and try to wrap around it (propolization), blocking the possibility of the correct measurement of acoustic pressure. To solve the problem, special cages for microphones can be used, or a metalic mesh as in [25; 26; 27]. Another solution is to measure the vibration generated by bees, as in the study [28], instead of sound. Analysis of the power spectral density (PSD) of audio signals recorded inside a hive shows that changes in spectral components for 110 Hz [25] indicate swarming. In the same study, it was concluded that with increased activity during swarming moments, there is an increase in the frequency of generated sound that is visible as the power spectral density as a shift of the frequency band from 100-300 Hz to a higher frequency band of 500-600 Hz. Besides the direct analysis of the frequency spectrum of audio recordings, automated methods using machine learning algorithms are nowadays extensively developed and tested. Research [29] applying the Convolutional Neural Network (CNN) to audio data recorded from hives shows that the swarming event can be detected at least 4 weeks before it occurs. In the study [27], audio signals were recorded for Carniola honeybee (_Apis mellifera Carnica_), and the classical Mel Frequency Cepstral Coefficients (MFCCs) were extracted as features for the multiclass classification task that predicts one of the three states of a hive, including the queenless state. For classification, several standard algorithms were used: support vector machines (SVMs), K-nearest neighbors (KNNs), logistic regression, random forest, and Multilayer Perceptron. The observation of increased drone activity [30] close to the entrance to a hive correlates with the time of year when they travel to the drone congregation areas. The fact can indirectly allow for building an early warning system for the beekeepers to prevent swarming, where half of the bee colony can escape with an old queen. Since the drone population has its peak around the same time when there is the highest swarming mood as claimed in [15; 19; 20; 21], it should be possible to build an early warning system based on the detection of the high flight activity of drones for audio signals recorded outside of a hive in close proximity to its entrance. There are methods to prevent swarming, such as taking away from the hive some of the produced honey frames to force the colony to focus on the foraging task. The beekeeper intervention can be suggested by a smart automatic system, based on bee activity analysis, for example, by the audio signal detection proposed in this paper. The causes of swarming are still poorly understood [15]. Beekeepers have reported that it can occur even when a colony is under constant monitoring and all preventive measures are immediately applied. The paper is divided into four main sections.