Artículo
Furnariidae Species Classification Using Extreme Learning Machines and Spectral Information
Albornoz, Enrique Marcelo
; Vignolo, Leandro Daniel
; Sarquis, Juan Andrés
; Martínez, César Ernesto
Fecha de publicación:
11/2018
Editorial:
Springer Verlag
Revista:
Lecture Notes in Computer Science
ISSN:
0302-9743
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Automatic bird species classification and identification are issues that have aroused interest in recent years. The main goals involve more exhaustive environmental monitoring and natural resources managing. One of the more relevant characteristics of calling birds is the vocalisation because this allows to recognise species or identify new ones, to know its natural history and macro-systematic relations, among others. In this work, some spectral-based features and extreme learning machines (ELM) are used to perform bird species classification. The experiments were carried on using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America) and werevalidated in a cross-validation scheme. The results show that ELM classifierobtains high classification rates, more than 90% in accuracy, and the proposed features overperform the baseline features.
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Articulos(INALI)
Articulos de INST.NAC.DE LIMNOLOGIA (I)
Articulos de INST.NAC.DE LIMNOLOGIA (I)
Citación
Albornoz, Enrique Marcelo; Vignolo, Leandro Daniel; Sarquis, Juan Andrés; Martínez, César Ernesto; Furnariidae Species Classification Using Extreme Learning Machines and Spectral Information; Springer Verlag; Lecture Notes in Computer Science; 11238 LNAI; 11-2018; 170-180
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