Detection and classification of right whale calls using an 'edge' detector operating on a smoothed spectrogram

Abstract

A detector has been developed which can reliably detect right whale calls and distinguish them from those of other marine mammals and industrial noise. Detection is a two stage process. In the first, the spectrogram is smoothed by convolving it with a Gaussian kernel and the 'outlines' of sounds are extracted using an edge detection algorithm. This allows a number of parameters to be measured for each sound, including duration, bandwidth and details of the frequency contour such as the positions of maximum and minimum frequency. In the second stage, these parameters are used in a classification function in order to determine which sounds are from right whales. The classifier has been tuned by comparing data from a period when large numbers of right whales were known to be in the vicinity of bottom mounted recorders with data collected on days when it was believed, based on ship and aerial surveys, that no right whales were present. Overall, the detection system is capable of picking out a high proportion of right whale calls logged by a human operator, while at the same time working at a false alarm rate of only one or two calls per day, even in the presence of background noise from humpback whales and seismic exploration. Although it is impossible to reduce the false alarm rate for individual calls to zero whilst still maintaining adequate efficiency, by requiring the detection of several calls within a set waiting time, it is possible to reduce false alarm rate to a negligible level.