Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42442
Title: Automatic classification of pinnipeds based on their vocalizations and fusion of cepstral features
Authors: Noda, J. J.
Travieso, Carlos M. 
Sanchez-Rodriguez, David 
Dutta, M. K.
Singh, A.
UNESCO Clasification: 33 Ciencias tecnológicas
3307 Tecnología electrónica
Keywords: kNN
LFCC
MFCC
Pinniped's call recognition
Sound classification
Issue Date: 2016
Conference: 3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016 
Abstract: Acoustic vocalizations are common in marine mammals which can be used for classification purposes. Pinnipeds are a group of carnivore mammals composed by seals, sea lions, and walruses. But although, there is a great interest in research literature about acoustic monitoring of marine mammals, the identification of pinnipeds trough experts systems has been poorly studied. This paper brings a novel method for the automatic taxonomic classification of pinnipeds using a fusion of Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), representing the acoustic signal in both low and high frequencies. In our experiment, we have used kNN for classification, achieving an average identification of 96.48% ± 9.17 over 18 pinniped species.
URI: http://hdl.handle.net/10553/42442
ISBN: 9781467391979
DOI: 10.1109/SPIN.2016.7566691
Source: 2016 3Rd International Conference On Signal Processing And Integrated Networks (Spin), p. 215-219
Appears in Collections:Actas de congresos
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