title: Automated tracking of dolphin whistles using Gaussian mixture probability hypothesis density filters.
abstract: This work considers automated multi target tracking of odontocete whistle contours. An adaptation of the Gaussian mixture probability hypothesis density (GM-PHD) filter is described and applied to the acoustic recordings from six odontocete species. From the raw data, spectral peaks are first identified and then the GM-PHD filter is used to simultaneously track the whistles' frequency contours. Overall over 9000 whistles are tracked with a precision of 85% and recall of 71.8%. The proposed filter is shown to track whistles precisely (with mean deviation of 104 Hz, about one frequency bin, from the annotated whistle path) and 80% coverage. The filter is computationally efficient, suitable for real-time implementation, and is widely applicable to different odontocete species.
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Highfield, Hants, SO17 1BJ, United Kingdom.
authors.forename: Pina
authors.initials: P
authors.lastname: Gruden
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Highfield, Hants, SO17 1BJ, United Kingdom.
authors.forename: Paul R
authors.initials: PR
authors.lastname: White