Understanding what psycho-physiological and behavioural factors influence aversiveness of sound in marine mammals is important for conservation and practical applications. The aim of this study was to determine predictors for impact of anthropogenic noise and to develop a target-specific predator deterrence system for use on fish farms. Three classes of stimuli were tested: 1.) grey seal underwater communication calls expected to be used in territorial defence, 2.) high duty-cycle moderately loud artificial sounds (some of which were based on models of unpleasantness for humans), 3.) brief, intense pulses designed to elicit the acoustic startle reflex. Communication calls had no deterrence effect but instead caused attraction responses. Tests with high duty-cycle artificial sounds showed that food-motivated animals habituate quickly, although sound exposure caused subtle changes in diving patterns over a longer time. Field trials using the same stimuli were used to determine avoidance thresholds but also indicated that sound features like ‘roughness’ play a role. The startle eliciting stimuli, however, had the most dramatic effects. To this stimulus most seals exhibited rapid flight responses, hauled out, sensitised and showed signs of fear conditioning. Startle thresholds were found to be 80-85 dB above the assumed hearing threshold. The data showed that startle thresholds are a crucial predictor for the occurrence of strong avoidance behaviour and suggests that the startle response evolved to increase an animal’s propensity for flight. Finally, a prototype predator deterrence system based on the startle sounds was developed to repel seals whilst not affecting toothed whales. In fish farm trials, seals were deterred at close ranges but local abundance of cetaceans did not change showing that it is possible to cause differential responses between species based on differences in their audiograms. The results are used to develop noise exposure criteria and to elucidate acoustic parameters that can be used to predict responses to anthropogenic noise.

Ocean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods that automatically detect and classify vocalization patterns of marine mammals. The first work performed is the classification of bottlenose dolphin calls by type. The extraction of salient and distinguishing features from recordings is a major part of this endeavor. To this end, two strategies are evaluated with real datasets provided by Woods Hole Oceanographic Institution: The first strategy is to use contour-based features such as Time-Frequency Parameters and Fourier Descriptors and the second is to employ texture-based features such as Local Binary Patterns (LBP) and Gabor Wavelets. Once dolphin whistle features are extracted for spectrograms, selection of classification procedures is crucial to the success of the process. For this purpose, the performances of classifiers such as K-Nearest Neighbor, Support Vector Machine, and Sparse Representation Classifier (SRC) are assessed thoroughly, together with those of the underlined feature extractors. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection