Investigation into the response of the auditory and acoustic communications systems in the Beluga whale (Delphinapterus leucas) of the St. Lawrence River Estuary to noise, using vocal classification
Date of Completion
January 2003
Keywords
Biology, Oceanography|Physics, Acoustics
Degree
Ph.D.
Abstract
Noise pollution has only recently become recognized as a potential danger to marine mammals in general, and to the Beluga Whale (Delphinapterus leucas) in particular. These small gregarious Odontocetes make extensive use of sound for social communication and pod cohesion. The St. Lawrence River Estuary is habitat to a small, critically endangered population of about 700 Beluga whales who congregate in four different sites in its upper estuary. The population is believed to be threatened by the stress of high-intensity, low frequency noise. One way to determine whether noise is having an effect on an animal's auditory ability might be to observe a natural and repeatable response of the auditory and vocal systems to varying noise levels. This can be accomplished by observing changes in animal vocalizations in response to auditory feedback. A response such as this observed in humans and some animals is known as the Lombard Vocal Response, which represents a reaction of the auditory system directly manifested by changes in vocalization level. ^ In this research this population of Beluga Whales was tested to determine whether a vocalization-as-a-function-of-noise phenomenon existed by using Hidden Markhov “classified” vocalizations as targets for acoustical analyses. Correlation and regression analyses indicated that the phenomenon does exist and results of a human subjects experiment along with results from other animal species known to exhibit the response strongly implicate the Lombard Vocal Response in the Beluga. ^
Recommended Citation
Scheifele, Peter Martin, "Investigation into the response of the auditory and acoustic communications systems in the Beluga whale (Delphinapterus leucas) of the St. Lawrence River Estuary to noise, using vocal classification" (2003). Doctoral Dissertations. AAI3118968.
https://digitalcommons.lib.uconn.edu/dissertations/AAI3118968