Using bioacoustic signals and Support Vector Machine for automatic classification of insects

Título:	Using bioacoustic signals and Support Vector Machine for automatic classification of insects
Autores/as:	Noda, Juan J. Travieso, Carlos M. Sanchez-Rodriguez, David Dutta, Malay Kishore Singh, Anushikha
Clasificación UNESCO:	33 Ciencias tecnológicas 240601 Bioacústica
Palabras clave:	Bioacoustic classification Insect sound recognition LFCC MFCC SVM
Fecha de publicación:	2016
Editor/a:	Institute of Electrical and Electronics Engineers (IEEE)
Conferencia:	3rd International Conference on Signal Processing and Integrated Networks (SPIN)
Resumen:	This work presents a new approach for automatic recognition of insects through intelligent systems. Insect species employ a set of sound signals for communication purposes which are specie-specific. Based on this fact, an acoustic signal recognition method has been designed to allow an efficient taxonomic classification of this animal group. In this paper, the sound signals have been characterized by Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs) to compare their efficacy. Then, a Support Vector Machine algorithm has been used for classification achieving an average success rate of 99.08% over 88 insect species.
URI:	http://hdl.handle.net/10553/42441
ISBN:	9781467391979
DOI:	10.1109/SPIN.2016.7566778
Fuente:	3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016, p. 656-659, (Septiembre 2016)
Colección:	Actas de congresos

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