Automatic syllables segmentation for frog identification system

Automatic recognition of frog sound according to particular species is considered a worthy tool for biological research and environmental monitoring. As a result, automatic recognition of frog sound offers many advantages rather than manual method that depending on physical observation procedure. This study evaluates the accuracy of frog sound identification from 12 species that recorded from Malaysia forest. By applying short time energy and short time average zero crossing rate, the frog sound samples are automatically segmented into syllables. A syllable feature extraction method i.e, Mel-Frequency Cepstrum Coefficients is employed to extract the segmented signal. Finally, nonparametric k-nearest neighbor classifier with Euclidean distance has been employed to recognize the frog species. A comparison between automatic segmentation and manual segmentation is applied and results show that automatic segmentation outperforms to identify the frog species with an accuracy of 97% compared to 82.33% for manual segmentation.