Call detection and extraction using Bayesian inference

Marine mammal vocalizations have always presented an intriguing topic for researchers not only because they provide an insight on their interaction, but also because they are a way for scientists to extract information on their location, number and various other parameters needed for their monitoring and tracking. In the past years field researchers have used submersible microphones to record underwater sounds in the hopes of being able to understand and label marine life. One of the emerging problems for both on site and off site researchers is the ability to detect and extract marine mammal vocalizations automatically and in real time given the copious amounts of existing recordings. In this paper, we focus on signal types that have a well-defined single frequency maxima and offer a method based on Sine wave modeling and Bayesian inference that will automatically detect and extract such possible vocalizations belonging to marine mammals while minimizing human interference. The procedure presented in this paper is based on global characteristics of these calls thus rendering it a species independent call detector/extractor.