Artículo
Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning
Fecha de publicación:
03/2017
Editorial:
Elsevier Science
Revista:
Ecological Informatics
ISSN:
1574-9541
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Over the last years, researchers have addressed the automatic classification of calling bird species. This is important for achieving more exhaustive environmental monitoring and for managing natural resources. Vocalisations help to identify new species, their natural history and macro-systematic relations, while computer systems allow the bird recognition process to be sped up and improved. In this study, an approach that uses state-of-the-art features designed for speech and speaker state recognition is presented. A method for voice activity detection was employed previous to feature extraction. Our analysis includes several classification techniques (multilayer perceptrons, support vector machines and random forest) and compares their performance using different configurations to define the best classification method. The experimental results were validated in a cross-validation scheme, using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America). The results show that a high classification rate, close to 90%, is obtained for this family in this Furnariidae group using the proposed features and classifiers.
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Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Citación
Albornoz, Enrique Marcelo; Vignolo, Leandro Daniel; Sarquis, Juan Andrés; Leon, Evelina Jesica; Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning; Elsevier Science; Ecological Informatics; 38; 3-2017; 39-49
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