Feature Extraction Based on Bandpass Filtering for Frog Call Classification

In this paper, we propose an adaptive frequency scale filter bank to perform frog call classification. After preprocessing, the acoustic signal is segmented into individual syllables from which spectral peak track is extracted. Then, syllable features including track duration, dominant frequency, and oscillation rate are calculated. Next, a k-means clustering technique is applied to the dominant frequency of syllables for all frog species, whose centroids are used to construct a frequency scale. Furthermore, one novel feature named bandpass filter bank cepstral coefficients is extracted by applying a bandpass filter bank to the spectral of each syllable, where the filter bank is designed based on the generated frequency scale. Finally, a k-nearest neighbour classifier is adopted to classify frog calls based on extracted features. The experiment results show that our proposed feature can achieve an average classification accuracy of 94.3% which outperforms Mel-frequency cepstral coefficients features (81.4%) and syllable features (88.1%).