## 3 Results and Discussion
### Recommendations  
In light of the findings obtained from this literature review, in order to improve the quality and reproducibility of the future work in bee acoustics analysis, we propose the following list of recommendations for future works:  
1. Provide exact information about bee species: As many studies did not mention what species of bees were used in their research (e.g., [45; 51; 59]), it becomes hard to replicate findings. As such, it is recommended that future works report this information for better comparisons and understanding of each bee species acoustic behaviour.
2. Apply audio denoising methods: Very few studies reported the use of noise removal steps prior to feature analysis. In realistic settings, ambient noise can contaminate the beehive audio, thus potentially leading to erroneous decisions. As such, future work should explore the use of noise removal algorithms, as well as test if existing tools remove discriminatory information from the recordings and if custom algorithms need to be built.
3. Consider environmental factors: Environment factors, such as temperature, humidity, and pollution can affect bee behaviour. In the studies that explored foraging behaviour, it was shown to be important to report the flowering species information, as this can affect beehive acoustics. Few works reported environmental factors, and future studies should attempt to incorporate additional sensor information to provide these additional contextual factors.
4. Consideration of hive opening for hive inspections: Most articles reviewed included one or more hive inspections. As hive openings can cause changes in hive acoustics, as well as internal temperature and humidity, not to mention the bees' defensive behaviour and smoke reactions, these could lead to outliers that could result in erroneous decisions. In future studies, hive opening metadata should be collected to allow for hive opening detectors to be built.  
5. Provide detailed information about the recording procedure: We know that the experiment setup has a direct effect on the audio samples and their quality. Therefore, reporting microphone frequency ranges, sampling rates, and any preprocessing is important to allow for comparisons between works to be made. Very few works reported these details; thus, future works should place some emphasis on these aspects.
6. Clear description of the machine learning pipeline: It is known that the procedure to train and test the efficacy of different machine learning algorithms is crucial information needed for replication. For example, changing the testing setup (e.g., cross-validation, versus train/test split versus leave-one-hive-out) can lead to very different models, as can different hyperparameter tuning strategies, different normalization schemes, or regularization methods. These details were seldom reported in the surveyed papers, thus future work should strive to provide a complete description of the testing setup.
7. Data and code sharing: As this is a new research field, there are still many unanswered questions that need to be answered. Sharing of data and code will be crucial for the field to grow and for studies to be replicated and benchmarked. Future works should prioritize the sharing of any newly-collected data, as well as any new codes developed. Researchers could also consider running some data processing and machine learning challenges, which have been shown to help advance emerging fields (e.g., see [120]).