title: Computer models for masked hearing experiments with beluga whales (Delphinapterus leucas).
abstract: Environmental assessments of manmade noise and its effects on marine mammals need to address the question of how noise interferes with animal vocalizations. Seeking the answer with animal experiments is very time consuming, costly, and often infeasible. This article examines the possibility of estimating results with software models. A matched filter, spectrogram cross-correlation, critical band cross-correlation, and a back-propagation neural network detected a beluga vocalization in three types of ocean noise. Performance was compared to masked hearing experiments with a beluga whale [C. Erbe and D. M. Farmer, Deep-Sea Res. II 45, 1373-1388 (1998)]. The artificial neural network simulated the animal data most closely and raised confidence in its ability to predict the interference of a variety of noise source with a variety of vocalizations.
authors.affiliation: Institute of Ocean Sciences, Sidney, British Columbia, Canada.
authors.forename: C
authors.initials: C
authors.lastname: Erbe
authors.forename: A R
authors.initials: AR
authors.lastname: King
authors.forename: M
authors.initials: M
authors.lastname: Yedlin
authors.forename: D M
authors.initials: DM
authors.lastname: Farmer
mesh_terms: D000818:Animals; D001309:Auditory Threshold / Q000502:physiology; D006309:Hearing / Q000502:physiology*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009622:Noise; D010470:Perceptual Masking; D014828:Vocalization, Animal / Q000502:physiology; D014907:Whales