Automatic recognition of frog calls using a multi-stage average spectrum

The automatic recognition of animal sounds is one of the powerful techniques for replacing the traditional ecological survey method that mainly depends on manpower, which is hence both costly and time consuming. This study developed an automatic frog call recognition system based on the combination of a pre-classification method of the syllable lengths and a multi-stage average spectrum (MSAS) method. In this system, the input frog syllables are first classified into one of the four groups determined by the pre-classification method according to syllable length. Then the proposed MSAS method is used to extract the standard feature template to analyze the time-varying features of each frog species and to recognize the input frog syllable by a template matching method. In all, 960 syllables recorded from 18 frog species are included in this study to evaluate the accuracy of the proposed frog call recognition system. The experimental results demonstrate that the proposed one-level (using the MSAS method only) and two-level (combining the syllable length pre-classification and MSAS methods) recognition methods can provide the best recognition accuracies of 91.9% and 94.3%, respectively, compared with other recognition methods based on dynamic time warping (DTW), spectral ensemble average voice prints (SEAV), k-nearest neighbor (kNN) and support vector machines (SVMs)