Detection of whale calls in noise: Performance comparison between a belugawhale, human listeners, and a neural network

This article examines the masking by anthropogenic noise of beluga whale ca lls. Results from human masking experiments and a software backpropagation neural network are compared to the performance of a trained beluga whale. T he goal was to find an accurate, reliable, and fast model to replace length y and expensive animal experiments. A beluga call was masked by three types of noise, an icebreaker's bubbler system and propeller noise, and ambient arctic ice-cracking noise. Both the human experiment and the neural network successfully modeled the beluga data in the sense that they classified the noises in the same order from strongest to weakest masking as the whale an d with similar call-detection thresholds. The neural network slightly outpe rformed the humans. Both models were then used to predict the masking of a fourth type of noise, Gaussian white noise. Their prediction ability was ju dged by returning to the aquarium to measure masked-hearing thresholds of a beluga in white noise. Both models and the whale identified bubbler noise as the strongest masker, followed by ramming, then white noise. Natural ice -cracking noise masked the least. However, the humans and the neural networ k slightly overpredicted the amount of masking for white noise. This is neg lecting individual variation in belugas, because only one animal could be t rained. Comparing the human model to the neural network model, the latter h as the advantage of objectivity, reproducibility of results, and efficiency , particularly if the interference of a large number of signals and noise i s to be examined. (C) 2000 Acoustical Society of America. [S0001-4966(00)01 007-9].