Robust unsupervised Tursiops aduncus whistle-event detection using gammatone multi-channel Savitzky–Golay based whistle enhancement

Abstract

Detecting whistle events is essential when studying the population density and behavior of cetaceans. After eight months of passive acoustic monitoring in Xiamen, we obtained long calls from two Tursiops aduncus individuals. In this paper, we propose an algorithm with an unbiased gammatone multi-channel Savitzky–Golay for smoothing dynamic continuous background noise and interference from long click trains. The algorithm uses the method of least squares to perform a local polynomial regression on the time–frequency representation of multi-frequency resolution call measurements, which can effectively retain the whistle profiles while filtering out noise and interference. We prove that it is better at separating out whistles and has lower computational complexity than other smoothing methods. In order to further extract whistle features in enhanced spectrograms, we also propose a set of multi-scale and multi-directional moving filter banks for various whistle durations and contour shapes. The final binary adaptive decisions at frame level for whistle events are obtained from the histograms of multi-scale and multi-directional spectrograms. Finally, we explore the entire data set and find that the proposed scheme achieves the highest frame-level F1-scores when detecting T. aduncus whistles than the baseline schemes, with an improvement of more than 6%.