# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 1 Introduction  
The taxonomic class Reptilia is formed by turtles, crocodiles, snakes, lizards, and tuataras, of which some are able of vocalize [1], but they do not do it often. As a result, there are few studies in literature about the reptile acoustic communications, and have been considered unimportant until recently. However, some geckos, crocodiles, and turtles are very active in producing vocalizations [2; 3], but their social roles are still not fully understood. Crocodiles are probably the most vocal reptiles, with a rich variety of hissing, distress, and threatening calls due to their close relation with birds, they are even capable of vocalizing in the egg before hatching [4]. Moreover, some species such as turtles, crocodiles, and alligators can emit sound both in air and underwater [5]. Reptiles emit vocalizations in a broad range of frequencies--they produce sounds mainly between 0.1-4 kHz--but some turtles, crocodiles, and also lizards are able to generate calls above 20 kHz [5; 6]. In addition, as a consequence of their behavior and small size, most reptiles can be very difficult to detect in the field using visual surveys [7], which can lead to an underestimation of species richness. Bioacoustic technologies are an efficient way to sample populations in extended areas where visibility is limited [8], so they may be able to provide additional data for reptile estimation. Traditional bioacoustic monitoring methods rely on human observers who categorize acoustic patterns according to soundsimilarities. However, this procedure is slow, and it depends on the observer's ability to identify species, which leads to bias [9]. Hence, machine learning techniques are being applied in many research areas to design automatic classification intelligent systems, such as mosquito identification based on morphological features [10], carbon fiber fabrics classification to minimize risks in engineering processes [11], automatic recognition of arrhythmias for the diagnosis of heart diseases [12], or this work where bioacoustic signals of reptile species are used for taxonomic classification. In recent decades, several techniques have been proposed to automate the acoustic classification of species through intelligent systems. For instance, [PERSON] et al. [13] successfully classified three bird and nine frog species by characterizing their calls with 11 variables: minimum and maximum frequency, call length, maximum power, and frequency of eight highest energy points in the call. Then, the results of three classification algorithms--Decision Tree (DT), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM)--were compared. In their work, SVM achieved an identification rate of 94.95%, outperforming DT and LDA, but the sample calls were selected manually. [PERSON] [14] used a Hidden Markov Model (HMM) to recognize the vocalizations of nine bird, ten frog, and eight cricket species. This approach employed the peak frequencies and bandwidth from the spectrogram to characterize the sound samples, getting high classification rates for each animal group individually, though it had difficulties coping with complex broad band calls. Another interesting approach can be found in [PERSON]'s research [15], where 50 different insect sounds were classified with an accuracy of 96.17%. In that work, Mel Frequency Cepstral Coefficients (MFCCs) were employed as features, and a Probabilistic Neural Network (PNN) was applied for classification. However, the sounds were taken from noise-free sections of the recorded files. More recently, [PERSON] et al. [16] recognized seven different species of bats by Gaussian Mixture Models (GMM), achieving a low average error of 1.8%, using a combination of linear and non-linear parameters. There is no doubt of the progress made in the field of bioacoustic identification to enable an efficient classification of species. However, a robust machine learning technique to recognize reptile calls has still not been found.