## 1 Introduction  
Researchers have found that more than 800 fish species are able to produce sounds for diverse purposes [1; 2]. Most of the sounds are emitted at low frequencies [3], usually below 1000 Hz. However, some pulses can reach 8 kHz [4; 5] or present more complex characteristics [6]. In addition, these emissions are typically broadband short-duration signals (see Figure 1). Fish generate sounds through several mechanisms, which depend on the species and a variety of circumstances, such as courtship, threats or defending territory [7]. Hence, most fish make species-specific acoustic signals that can be gathered using passive acoustic sensors (hydrophones). Passive acoustic surveys are widely used to monitor presence/absence of marine fauna, tracking their movement or estimating seasonal distribution, especially in the study of marine mammals [8; 9]. These recordings typically include noise from the natural environment and anthropogenic sources [10], which have to be suppressed to focus on biological signals. Anthropogenic noise has become one of the greatest sources of background noise in oceans, which is produced by human activity: commercial shipping, oil extraction, fishing, etc. Moreover, passive acoustic sensors offer advantages over visual monitoring systems in waters with poor visibility and with adverse weather conditions [11]. Several techniques have been applied to characterize acoustic vocalizations in wildlife animals for automatic detection and classification. Exhaustive studies have been conducted in birds [12], amphibians [13], insects [14] and bats [15] with varying degrees of success. As for marine fauna, some works can be found in the literature concerning the acoustic identification of whales and dolphins. [PERSON] et al. [16] developed a classification system to work with fragmented whistle detections of four _adoncetes_, achieving a 94% classification rate. However, when the number of species was increased, system accuracy dropped heavily. In [17], four types of dolphin calls were identified using a Fourier Descriptor (FD) to characterize the whistlers shape. Then, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) were employed as classifiers. In the end, statistical significant results were not obtained due to the small corpus. Different fish species are mainly recognized by sonar images based on shape and geometric features or by computer vision technologies. For instance, in [18], three species of fish were identified using acoustic echo sounder surveys and applying the SVM classifier. Sonar echoes were parametrized by a set of features: morphology, acoustical energy, bathymetry and school-shore distance. In this work, a classification rate of 89.95% was achieved. As for the visual techniques, in [19], ten species of fish were successfully classified by a Balance-Guaranteed Optimized Tree (BGOT) using a combination of features such as color, shape and texture image properties with 95% accuracy. Nevertheless, a robust intelligent system that identifies fish species by their acoustic signals has still not been found. Most previous efforts have limited their research to the spectro-temporal characterization of sound production, and the authors are only aware of a few studies [20; 21] that have utilized the unique acoustic properties of fish for automatic identification. In [20], [PERSON] et al. analyzed the sound clicks of the _Tilapia mariae_ fish by applying logistic regression and parameterizing the signal with a vector of six spectro-temporal features (STF). However, the syllables for training purposes were manually selected. Meanwhile, [PERSON] et al. [21] presented a method that automatically identifies acoustic emissions of two _scientid_ fish by threshold decision. This approach used pitch strength, drumming frequency and short/long term partial loudness as features producing satisfactory results, but the proposed system was hardly scalable. Figure 1: Fish acoustic vocalization spectrogram.