## 3 Dataset Design
### Methodology of Design of the Dataset
That was achieved by plotting the spectrogram of each of the segmented files and, according to the energy, classifying them as silences if there was no signal on the bat call frequency range. All the silences were added into the new class created (silences). Figure 5 illustrates a comparison between a file containing a bat call (top spectrogram) and silence (bottom spectrogram). **Step III:** Windowing: Application of a 400 ms Hamming window [44] to minimize abrupt changes between the beginning and the end of the signal. **Step IV:** Fourier transformation of each of the audio segments to obtain the frequency domain representation of the power spectra of the data [42]. **Step V:** Once the files were analyzed and the peculiarities of each one of the calls were observed, it was clear that low frequencies disrupted the analysis. For that reason, only the higher 70% of the frequencies of the obtained audio segments containing calls were analyzed. **Step VI:** To achieve a more precise analysis of the remaining higher frequency signal, the aggregation of the values of each of the columns that compose the spectrogram of the split recordings was computed in order to obtain an exact representation of the most powerful points. **Step VII:** Since the pulses have higher energy levels than the intervals of silence, the integration of the columns in which the pulses are found has higher values. To perform an accurate splitting of the call and silence fragments, the boundary to fragment the audio has to be defined. Five categories have been defined, which state the power relation between the peak and the average values within the audio file. The five thresholds are set to 5%, 10%, 15%, 20%, and 25%. For each file, the five defined thresholds are plotted above the computed integration of the columns. The threshold to use in each case has been set to the closest value in order to separate properly between the calls and the silences. **Step VIII:** The features extraction has been made with a Mel Cepstrum inspired filter bank, using 15 filters that start at the frequency of 38 kHz and finish at 80 kHz, as shown in Figure 10. All the filters were the same width and had a 40% overlap. The traditional MFCC filters, which were designed to approximate the human auditory system's response [22], have been replaced in order to analyze the frequencies of interest. The filter bank used has a linear structure, with all the filters being separated within a linear scale instead of a Mel frequency scale, as a first approach in this work, assuming that in the future other options can be tested. The height and width of all the filters were kept constant, so all the filters have a unitary area, to allow all the frequency distribution to have the same weight in the detection. From each of the filters, a coefficient has been extracted, resulting in a feature set with 15 coefficients. Figure 6 contains a visual representation of the steps that have been followed in the process. As a result of the split, 1465 segmented files of 400 ms length were generated from the initial audio files. Then, the segmented files that contained the bat calls were split, separating the call pulses and the silences between them. Silence comprises 94% of the total dataset, and it includes both the silent parts between the pulses and the 400 ms silent fragments. Each of the two bat species have the 3% each in the total dataset. The design of the dataset has the final goal of its use to train, validate and test a feature extraction and machine learning algorithm, in order to improve the accuracy of the identification, especially in short temporal pieces of data. Differentiating the silences from the parts where a bat call was found has resulted in a clearer dataset. As we can see in Figure 7, this has resulted in 5857 files from _Pipistrellus pipistrellus_ and 5928 from _Pipistrellus pygmaeus_. The total length of the recordings used is 1073.2 s (40.97 s of _Pipistrellus pipistrellus_, 42.66 s of _Pipistrellus pygmaeus_, and 989.57 s of silence).