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.
['Juan J. Noda Arencibia', 'Carlos M. Travieso', 'David Sánchez-Rodríguez', 'Malay Kishore Dutta', 'Garima Vyas']
Automatic classification of frogs calls based on fusion of features and SVM