This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species.

Fish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.

Nowadays, human activity is considered one of the main risk factors for the life of reptiles and amphibians. The presence of these living beings represents a good biological indicator of an excellent environmental quality. Because of their behavior and size, most of these species are complicated to recognize in their living environment with image devices. Nevertheless, the use of bioacoustic information to identify animal species is an efficient way to sample populations and control the conservation of these living beings in large and remote areas where environmental conditions and visibility are limited. In this chapter, a novel methodology for the identification of different reptile and anuran species based on the fusion of Mel and Linear Frequency Cepstral Coefficients, MFCC and LFCC, is presented. The proposed methodology has been validated using public databases, and experimental results yielded an accuracy above 95% showing the efficiency of the proposal.

Bioacoustic research of reptile calls and vocalizations has been limited due to the general consideration that they are voiceless. However, several species of geckos, turtles, and crocodiles are abletoproducesimpleandevencomplexvocalizationswhicharespecies-specific.Thisworkpresents a novel approach for the automatic taxonomic identification of reptiles through their bioacoustics by applying pattern recognition techniques. The sound signals are automatically segmented, extracting each call from the background noise. Then, their calls are parametrized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC) to serve as features in the classification stage. In this study, 27 reptile species have been successfully identified using two machine learning algorithms: K-Nearest Neighbors (kNN) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 97.78% and 98.51%, respectively.