## 3 Dataset Design
### Methodology of Design of the Dataset  
After analyzing the files, we observed that in the frequencies of interest (8-120 kHz) the only events that took place were the bat calls. For this reason, the dataset has been split into three classes, one for each of the species and one for the parts of the recording where a silence was identified. In the case of other events being detected on these frequencies, they would be tagged in different categories. Bat calls are very short and they are preceded by long silent intervals, resulting in a nonbalanced dataset. The time domain contribution of each of the categories corresponds to a 3% of _Pipistrellus pipistrellus_, another 3% of _Pipistrellus pygmaeus_, and a 94% of silence. All the recordings have been split into 400 ms audio fragments. The division of the files has been made because the analysis of shorter audio fragments results in a more exact study, allowing recordings to be separated more accurately between calls and silences. However, the audio files need to have a minimal length, as more features can be perceived from longer files, such as the interpulse duration. Due to the call frequency, two to five consecutive calls are included in the 400 ms audio fragments, allowing for a more precise characterization. A longer length would lead to an inaccurate study of the pulse units, and a shorter would lead to confusion as to which set of pulses the calls belonged to. Figure 2 contains the temporal signal representation of an audio file and its spectrogram. This recording corresponds _Pipistrellus pipistrellus_. We can appreciate how the parts which include sounds emitted by bats generate the equivalent pulse on the frequency domain. There is a clear  
differentiation between the bat pulses and the silent parts, which we will use to split the audio files in order to have a more accurate dataset. Another important detail about the recordings is the fact that the bounce of the signal to the recorder caused an echo that was also recorded, as shown and indicated in Figure 3. Therefore, the vast majority of calls were proceeded by their reflection and for a better parameterization of data, we have included them into the call fragments [48]; this way the authors assume that this issue has to be taken into account by the analysis conducted. Figure 3: Echo produced by the bounce of the signal to the microphone. Figure 2: Identification of bat call pulses on an sample audio file. Figure 4 contains the spectrogram of two sample files of each species before separating the call pulses and the silences between them. One of them has been expanded in order to appreciate the frequency and the amount of time of each pulse of the call. The main differences between the calls of the two bat species are the energy of the higher frequencies [6], the separation between a pulse and the following (interpulse duration), their duration, and the start and ending frequencies [18]. The dataset created divides these audio files into the parts where bat calls were produced and the parts containing silences. The process that has been followed in order to segment and annotate the audio files can be divided into eight main steps, detailed in the following Algorithm 1. Data division process is described in steps I to VII. Step VIII details the parameterization method that has been used to study the separability of the calls. Figure 4: Spectrograms of the species. The left hand figures illustrate a close view of the call pulse and the measurement of its bandwidth and duration (85 Power/Frequency). The right hand figures, represents 400 ms’ of an audio fragment containing a bat call. **Step I:** The first step taken has been to split the original audio files into 400 ms length samples, in order to obtain shorter and easier to analyze files, with a maximum of 4-5 bat calls in each file. **Step II:** Once the files were split into 400 ms fragments, we proceeded to identify those in which no call pulses were found, which were labeled as silent.