Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/37111
Título: Automatic anuran identification using noise removal and audio activity detection
Autores/as: Alonso, Jesús B. 
Cabrera, Josué
Shyamnani, Rohit
Travieso González, Carlos Manuel 
Bolaños, Federico
García, Adrián
Villegas, Alexander
Wainwright, Mark
Clasificación UNESCO: 240601 Bioacústica
120325 Diseño de sistemas sensores
Palabras clave: Bioacoustical identification
Biodiversity monitoring
Species richness
Ecological indices
Environmental audio, et al.
Fecha de publicación: 2017
Publicación seriada: Expert Systems with Applications 
Resumen: The use of bioacoustics to identify animal species has huge potential for use in biology and conservation research. Fields that could be greatly enhanced by the use of bioacoustical techniques include the study of animal behavior, soundscape ecology, species diversity assessments, and long-term monitoring- for example to further our understanding of the conservation status of numerous species and their vulnerability to different threats. In this study, we focus primarily, but not exclusively, on the identification of anuran vocalizations. We chose anurans both because they tend to be quite vocal and because they are considered indicators of environmental health. We present a system for semi-automated segmentation of anuran calls, based on sound enhancement method that uses Minimum-Mean Square Error (MMSE) Short-Time Spectral Amplitude (STSA) estimator and noise suppression algorithm using Spectral Subtraction (SS), and an automated classification system for 17 anuran species based on Mel-Frequency Cepstrum Coefficients (MFCC) and the Gaussian Mixture Model (GMM). To our knowledge this is the first study that applies this combination of methods for animal identification. This technique achieves accuracies of between 96.1% and 100% per species. Experimental results show that the semi-automated segmentation technique performs better than automated segmentation systems, improving the average success rate to 98.61%. The effectiveness of the proposed anuran identification system in natural environment is thus verified. This work presents a first approach to future tools which can signify a significant advance in the procedures to analysis in a semiautomatic or even in an automatic way to analysis indicators of environmental health based on expert and intelligent systems
URI: http://hdl.handle.net/10553/37111
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2016.12.019
Fuente: Expert Systems with Applications[ISSN 0957-4174],v. 75, p. 83-92
Colección:Artículos
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