## 5 Conclusions and Future Work  
There is the possibility of training, validating and testing an algorithm using manually labelled bat calls data capable of identifying the two targeted species, and even the silences in between the calls. This fact encourages the team to keep on working not only using laboratory experiments, but also with real operation experiments in urban environments, where ultrasonic background noise are more common--and so would make bat call identification harder--and the algorithm would have to deal with other species. From the initial dataset analysis we can conclude that in some cases the two bat species are not easily separable, as they have many common patterns, such as similar frequencies of the calls or their frequency. Splitting the audio files into bat calls and silence fragments provides us with a more precise analysis in the dataset design. As that the studied audio files were split according to the power relation between their peak value and their average value in the first classification, the files have been divided into the ones that present some type of call and the ones that do not. Although this has been a manual and precise task, it has helped to improve the reliability of our dataset, which is published and available for a further analysis [19; 20]. Although there is a wide range of features that can be obtained from the audio fragments, those chosen have been designed and implemented to fit with specifications of the problem. The decision to include the pulse bounce to the microphone to the call enables the system to work with more realistic data. If we had not considered this bounce, the system would have got worse results, as it would have had considered the bounce as an independent pulse. The further automatic classification had to be adapted to the data resulting from this parameterization, so two different methodologies were used. The obtained results show a common pattern of both classification models to achieve good results on classifying silent fragments correctly. The analysis of these results show that, surprisingly, these data are better represented by using image processing methods than by being processed by the CNNs. In the future, also a two-stage detection algorithm could be implemented, discarding all analyzed windows corresponding to silences, and using the machine learning algorithms to identify only pieces of data with bat call signal. Regarding future applications of the developed system, we have to take into consideration that bats are very selective when it comes to the locations they inhabit, so their presence in a particular ecosystem reveals lots of information about both the area's biological environment and its contribution to the conservation of bat species. They are important bioindicators of some of the most relevant state-of-the-art topics in biology, such as habitat loss and climate change [58]. Their presence in a habitat can also provide a great financial relief for agricultural industries thanks to their contribution as pest managers [2]. Such important values demonstrate the great importance of their monitoring for a better understanding of bats ecology, and as a source of ecological information about a particular habitat. For that reason, the developed approach for an automatic detection and identification of the presence of bats can be applied to many studies of the characteristics of a particular environment. The quality of the habitat of a city is currently a popular field of study, and in this sense, studies as the one presented in his work can improve the knowledge of the environment in urban areas. One of the aims of the smart cities is to control habitat welfare through the placement of sensors acting as bioindicators. They serve to monitor aspects such as air quality or weather, which are key elements of the global ecosystem. These sensors are responsible for collecting the data to be analyzed and as a source of information for the city acting consequently.