Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/47594
Título: Automatic classification of frogs calls based on fusion of features and SVM
Autores/as: Noda Arencibia, Juan J.
Travieso, Carlos M. 
Sánchez-Rodríguez, David 
Dutta, Malay Kishore
Vyas, Garima
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Sound Classification
Frogs Call Recognition
Mfcc
Data Fusion
Svm
Fecha de publicación: 2015
Publicación seriada: 2015 Eighth International Conference On Contemporary Computing (Ic3)
Conferencia: 8th International Conference on Contemporary Computing, IC3 2015 
Resumen: This paper presents a new approach for the acoustic classification of frogs' calls using a novel fusion of features: Mel Frequency Cepstral Coefficients (MFCCs), Shannon entropy and syllable duration. First, the audio recordings of different frogs' species are segmented in syllables. For each syllable, each feature is extracted and the cepstral features (MFCC) are computed and evaluated separately as in previous works. Finally, the data fusion is used to train a multiclass Support Vector Machine (SVM) classifier. In our experiment, the results show that our novel feature fusion increase the classification accuracy; achieving an average of 94.21% ± 8,04 in 18 frog's species.
URI: http://hdl.handle.net/10553/47594
ISBN: 9781467379489
ISSN: 2572-6110
DOI: 10.1109/IC3.2015.7346653
Fuente: 2015 8th International Conference on Contemporary Computing, IC3 2015 (7346653), p. 59-63
Colección:Actas de congresos
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