## 3 Dataset Design
### Raw Data Description, Labeling, and Analysis  
One of the advantages of bioacoustics are that it is a noninvasive methodology where you can identify bat species without handling them. The raw recording files are a very strong tool to work with bats if you use them appropriately [6; 17]. One point to take into account is that the recordings can be taken under different environmental variables which can not be controlled. However, there also are some disadvantages as not always all bat species can be identified.  
Nevertheless, bat calls can vary depending on factors such as habitat, age, sex, the presence of conspecifics or even local ambient noise [9; 40]. Therefore, it is really important to know in which environment and under which conditions were the files are recorded, specially, for the correct identification of bat species, because parameters of bat calls can be affected. Although, when you use auto-ID software these variables are not considered, so the randomness of the recording files is already in use. With this assumption, in our dataset, all the recordings are considered without taking into account the different environmental conditions, with diverse distances and qualities of the recordings.  
The original labeling of the raw ultrasonic data--which we consider as ground truth in this system--is a mixture of automatic and manual labeling. The system saves the samples recorded with the name of the species identified, but afterwards, an expert (in this case, coauthor [PERSON]) opened all and each of the files and analyzed manually, without conditioning this analysis to the previously label generated by the automated system.  
Our dataset is composed of 662 audio files that have been collected in several recording campaigns, which have been conducted by colleagues from the Universidad Complutense de Madrid (UCM). Two-hundred-and-sistry-six of the files contain recordings of _Pipistrellus pipistrellus_ and 396 contain recordings of _Pipistrellus pygmaeus_ species. They were recorded by an Echo Meter Touch Pro 1 bat detector (Wildlife Acoustics) [41] in the Guadarrama Mountains between 2016 and 2018. There is a minor ultrasound noise interference in this area, hence the signals of the range of frequencies studied were clear, especially when compared to any urban recordings. All sequences were recorded as full-spectrum in WAV format, gain medium, sampling rate of 384 kHz, trigger of 4 s, division ratio of 1/10, sensitive sensitivity, and medium trigger sensitivity. Kaleidoscope (Wildlife Acoustics, Inc.) was used for filtering noise from bat calls. The filter settings specified a signal of interest between 8 and 120 kHz and 2 to 500 ms and with a minimum of two calls per sequence. Each sequence was split to a maximum duration of 5 s, in order to standarize the bat call study. BatSound 4 Software was used to analyze the WAV files. The recordings were analyzed using a sampling frequency of 44.1 kHz, with 16 bits/sample and a 512 pt. fast Fourier-transform (FFT) [42; 43] with a Hamming window [44] for analysis [45]. At least, two echolocation calls were analyzed at random from each sequence. The following parameters were measured manually [46] from each call to identify the species [6; 47]: call structure, start frequency, end frequency, frequency of maximum energy, duration, and interpulse interval.