Automated tracking of dolphin whistles using Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters

Gruden, Pina and White, Paul R. (2016) Automated tracking of dolphin whistles using Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters. Journal of the Acoustical Society of America, 1-47. (doi:10.1121/1.4962980).

Record type: Article

Abstract

This work considers automated Multi Target Tracking (MTT) of odontocete whistle contours. An adaptation of 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 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.

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