Classification of Anurans Based on Vocalizations for Pervasive Environmental Monitoring

• Juan Gabriel Colonna UFAM
• Eduardo Freire Nakamura UFAM / FUCAPI
• Eulanda Miranda dos Santos UFAM

Abstract

In this work, we developed an automatic system for classifying anura (frogs and toads) based on their vocalizations. Each bioacustic signal has been segmented on the first stage into smaller units called “syllables”, followed by a preprocessing step, composed by a pre-emphasis filter and a Hamming window that prepares the signal for feature extraction. We used Mel-fourier Cepstral Coefficients (MFCCs) to represent the acoustic signals and two classifiers were evaluated: kNN and SVM. In our experiments, we have achieved a classification rate of 98.97%, which shows that the MFCC, usually used in speech recognition, can be used for the recognizing anura species as well. The anuran recognition rate was improved in 16.09%, using SVM and MFCCs, compared with results found in literature.

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