Impacts of environment-dependent acoustic propagation on passive acoustic monitoring of cetaceans

Abstract

Significant effort has been made over the last few decades to develop automated passive acoustic monitoring (PAM) systems capable of classifying cetaceans at the species level; however, these systems often require tuning when deployed in different environments. Anecdotal evidence suggests that this requirement to adjust a PAM system's parameters is partially due to differences in the acoustic propagation characteristics. The environmentdependent propagation characteristics create variation in how a cetacean vocalization is distorted after it is emitted. If these difference are not accounted for it could reduce the performance of automated PAM systems. An aural classifier developed at Defence R&D Canada (DRDC) has been used successfully for inter-species discrimination of cetaceans. Accurate results are obtained by using perceptual signal features that model the features employed by the human auditory system. In this thesis, a combination of an at-sea experiment and simulations with modified bowhead and humpback whale vocalizations was conducted to investigate the robustness of the classifier performance to signal distortion as a function of propagation range. It was found that in many environments classification performance degraded with increasing range, largely due to decreased signal-to-noise ratio (SNR); however, in some environments as much as 40 % of the performance reduction was attributed to signal distortion resulting from environment-dependent propagation. It was found that sound speed profiles resulting in considerable boundary interaction were important for producing sufficient signal distortion to affect PAM performance, relative to the impacts of SNR. Therefore, in some environments the ocean acoustic properties should be taken into account when characterizing performance of automated PAM systems. For the environments in which signal-to-noise issues dominate, the use of multi-element arrays is expected to increase the performance of automated recognition systems beyond the minor improvements to be gained from adjusting a PAM system's parameters. Nonetheless, propagation modelling should be used to complement PAM experiments to account for bias in probability of detection estimates resulting from environment-dependent acoustic propagation.