Comparative Study on Different Classifiers for Frog Identification System Based on Bioacoustic Signal Analysis

species produce skin secretions of an amino acid compound called peptides that can produce several avenues of research with application for human medicine. Instead of depending on physical observation procedure to identify the particular species, this study proposes an automated frog identification system based on bioacoustic signal analysis. Experimental studies of 1260 audio data from 28 species of frogs from the Internet and Intelligent Biometric Group, Universiti Sains Malaysia, IBG, USM databases are used in this study. These audio data are then corrupted by 10dB and 5dB noise. A syllable feature extraction method i.e. Mel-Frequency Cepstrum Coefficients (MFCC) employed to extract the segmented signal. Subsequently, three classifiers i.e. Support Vector Machine (SVM), Sparse Representation Classifier (SRC) and Local Mean k-Nearest Neighbor with Fuzzy Distance Weighting (LMkNN-FDW) are developed in order to evaluate the performance of the identification system. The experimental results show that LMkNN-FDW outperforms SVM and SRC in clean SNR by 97.54% and 87.2% for the Internet and IBG, USM databases, respectively.

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