Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/37100
Título: Fusion of linear and mel frequency cepstral coefficients for automatic classification of reptiles
Autores/as: Noda, Juan J.
Travieso-González, Carlos M. 
Sánchez-Rodríguez, David 
Clasificación UNESCO: 240601 Bioacústica
330702 Electroacústica
240114 Taxonomía animal
Palabras clave: Biological acoustic analysis
Bioacoustic taxonomy identification
Reptile vocalization
Frequency cepstral coefficients
SVM, et al.
Fecha de publicación: 2017
Publicación seriada: Applied Sciences (Basel) 
Resumen: 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 able to produce simple and even complex vocalizations which are species-specific. This work presents 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.
URI: http://hdl.handle.net/10553/37100
ISSN: 2076-3417
DOI: 10.3390/app7020178
Fuente: Applied Sciences (Basel) [ISSN 2076-3417], v. 7 (2), article number 178
Colección:Artículos
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