Fusion of Linear and Mel Frequency Cepstral Coefﬁcients for Automatic Classiﬁcation of Reptiles
AbstractBioacoustic 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. View Full-Text
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Noda, J.J.; Travieso, C.M.; Sánchez-Rodríguez, D. Fusion of Linear and Mel Frequency Cepstral Coefﬁcients for Automatic Classiﬁcation of Reptiles. Appl. Sci. 2017, 7, 178.
Noda JJ, Travieso CM, Sánchez-Rodríguez D. Fusion of Linear and Mel Frequency Cepstral Coefﬁcients for Automatic Classiﬁcation of Reptiles. Applied Sciences. 2017; 7(2):178.Chicago/Turabian Style
Noda, Juan J.; Travieso, Carlos M.; Sánchez-Rodríguez, David. 2017. "Fusion of Linear and Mel Frequency Cepstral Coefﬁcients for Automatic Classiﬁcation of Reptiles." Appl. Sci. 7, no. 2: 178.