OPTIMIZING MODELS OF DOLPHIN AUDITORY SENSITIVITY USING EVOLUTIONARY COMPUTATION

Abstract

ABSTRACT Object classifiers that attempt to mimic dolphin echolocation require an auditory weighting function representative of dolphin peripheral auditory processing. An evolutionary program (EvPg) was used to fit the frequency-dependent output of a bank of bandpass filters to the auditory sensitivity of the bottlenose dolphin, Tursiops truncatus. Pseudo-Gaussian (PG) and rounded exponential (ROEX) functions were used to describe individual filter shapes. Variables determining the number of filters per model, overall filter shape and amplitude scaling were submitted to the EvPg for optimization. Maximum deviation (P e ) between model output and the sensitivity of the dolphin was used as a measure of similarity between the two, i.e., lower P e indicated a greater similarity. The number of filters converged upon 37 for all ROEX models and ≤ 45 for all PG models. The P e of the best-performing PG model was 0.08, and for all ROEX models was 0.13. Greatest deviations typically occurred below 5 kHz and above 130 kHz. Relative audiometric sensitivity of a dolphin ear model has been improved relative to previous models, thereby providing an auditory weighting function more representative of dolphin peripheral auditory processing. This model will be applied to further investigate how dolphins use echolocation to discriminate among objects.