## 2 Background and Related Works  
[PERSON] et al. (2021) [31] specified that, in recent decades, sound has been recognized as a universal semiotic vehicle that represents an indicator of ecological structures and that is a relevant tool for describing how animal dynamics and ecosystem processes are affected by human activities. [PERSON] [32] compiled the fundamentals of various processes in animal communication, in community aggregation and in long-term monitoring, as well as in several processes of interest in ecology, providing space for soundscape ecology and ecoacoustics. [PERSON] et al. (2019) [33] explored various ecoacoustic practices, such as the use of various acoustic indices that reflect different attributes of the soundscape and recording collection methods. [PERSON] et al. (2020) [34] explored the problem of acoustic recognition of two frog species by using long-term field recordings and machine-learning methods. Acoustic data were extracted from 48 h of field recordings under different weather conditions. These data were used to conduct experiments and to assess recognition performance. The labeling task of frog chorusing was performed manually by trained ecologists who proposed, as features, spectral acoustic indices extracted from the recordings' spectrograms. For recognition experiments, they used the following as supervised learning algorithms: support vector machine (SVM), k-nearest neighbors (kNN) and random forest. The best score reported was 82.9% of accuracy obtained with the SVM classifier and combinations of synthetic and real-life data. [PERSON] et al. (2021) [35] report that there is evidence that certain species at birth produce calls that are important in mother-child communication that are characterized mostly by calls of low frequency, multi-harmonic and with specific temporal pattern. As they grow, these songs grow shorter in duration and their frequency rises. This is an evidence that indicates that the sonic characteristics of these species change with age, and that this should, therefore, be taken into consideration. [PERSON] et al. (2020) [36] developed a task for the acoustic recognition of birds and specified that this requires large amounts of audio recordings in order to be used by machine-learning methods. In this case, the main problem is the processing time. Theyaddressed this issue by evaluating the applicability of three data reduction methods. Their hypothesis was based on the notion that data reduction could highlight the central characteristics of features without the loss of recognition accuracy. The investigated methods were random sampling, uniform sampling and piecewise aggregate approximation. They used Mel-frequency cepstral coefficients (MFCC) as features and hidden Markov models (HMM) as learning algorithms. The most advantageous method reported was uniform sampling, with a 99.6% relative reduction in training time. [PERSON] and [PERSON] (2018) [37] addressed the classification of 15 different acoustic scenes, both outdoors and indoors, and the problem of expensive computation time in the training stage. They used the architecture of a deep multilayer perceptron as the baseline system. For the experiments, they employed the datasets from the 2017 IEEE AASP Challenge on the Detection and Classification of Acoustic Scenes and Events (DCASE) and 40 mbe (log of Mel-scale frequency energy) features as feature vectors, extracted every 40 ms of frame length and overlapped at 50%. They divided the dataset into three parts for training (60%), validation (20%) and testing (20%), and each trained model was evaluated by using a four-fold cross-validation approach. They reported an averaged overall result of 74% accuracy for the baseline system and a computation time of 20 min 40 s. When they used the Apache Spark MLlib platform along with the baseline system, they achieved 79% accuracy and a computation time of 0 min 55 s.  
[PERSON] et al.