## 3 Results and Discussion
### Study Goal
#### 3.2.1 Honeybees  
As a first step in honey bee analytics, one must first discriminate bee sounds from non-bee sounds, such as wind, rain, and urban sounds. Therefore, one of the common goals in the included papers is to develop bee activity detection algorithms for (i) classification of bee and non-bee sound as a preprocessing step [41; 48], and potentially use the processed audio for detecting swarming and queen bee absence, and (ii) detection of bee foraging for pollen or nectar [45]. The authors in [41], for example, used a machine learning framework to discriminate bee buzzing from cricket chirping and ambient noise. In [45], in turn, a bee detection system based on the harmonic characteristic of buzzes recorded using microphones placed in the orchard trees was designed for pollination mapping and to evaluate pollen production. A recent study has shown that hive acoustics could be useful to measure hive strength [49], defined as the number of frames within a hive that were at least 60% covered by bees, disease type, and disease severity. While the disease types were not mentioned in the study, four disease severity levels were considered, namely, none, low, moderate, and severe. The designed system is a semi-supervised generative-prediction network (GPN) that takes advantage of environmental variables such as temperature, humidity, and air pressure, alongside labelled and unlabelled audio. The average Huber loss [100] for predicting number of full frames and disease severity were around 0.0166 and 0.0331, respectively, while the accuracy of disease type was around 78%. Queen bees are essential for colony survival and thriingness. The death of the queen, or a serious decline in her health, will usually lead to the colony producing a new queen, unless a new queen is introduced by the beekeeper. As such, one of the most active research topics in bee audio analysis has been the detection of a queen bee within the hive. Articles that aimed to detect queen bee presence mostly relied on frequency domain features. While the work in [52; 57] aimed at reporting differences between a hive with a queen and a queenless hive, the work in [53; 55; 53] relied on machine learning algorithms to classify between these two states (presence/absence). Another common goal has been swarming detection. During swarming, a colony produces a new queen, and the old queen leaves her colony with approximately half of the worker bee population to start a new colony. Swarming is part of the reproduction cycle of the colony and typically occurs when resources are plentiful. After mating, the younger queen takes over the colony that is left behind and is tasked with growing it. With swarming, the result will be two smaller colonies that will use most of their energy for colony growth, rather than honey harvesting. If weather conditions are not suitable (e.g., close to winter), this can lead to colony deaths. Moreover, if not well controlled by the beekeeper, swarming can also have a significant economical impact on honey production. The work in [20; 21; 59; 60; 61; 62; 63; 64] showed that swarming prediction could be predicted using a remote and automated acoustics-based system. Using power spectral density, it was shown that the power increases by around 200 Hz when approaching the swarming period [20; 60]. Furthermore, it is known that honey bees will collect nectar or honey from any source they can find, and that includes a weak, or poorly guarded, colony. The process of invading another hive to steal the stored honey is referred to as \"robbing\", and it can occur anytime during the year, but it is most evident in the late summer or early fall, especially during a nectar dearth [101]. Authors in [66] showed that hive robbing could be detected via video and audio recordings of a beehive. A Beemon system [102] was placed right above the entrance of the hive with a camera facing down to record the videos and a microphone was placed inside the hive.