Automating multi-target tracking of singing humpback whales recorded with vector sensors.

Passive acoustic monitoring is widely used for detection and localization of marine mammals. Typically, pressure sensors are used, although several studies utilized acoustic vector sensors (AVSs), that measure acoustic pressure and particle velocity and can estimate azimuths to acoustic sources. The AVSs can localize sources using a reduced number of sensors and do not require precise time synchronization between sensors.

Using deep learning to track time × frequency whistle contours of toothed whales without human-annotated training data.

Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including species classification, identification, and density estimation. Deep-learning-based methods, which train models using analyst-annotated whistles, offer a promising way to reliably extract whistle contours.

Tracking time differences of arrivals of multiple sound sources in the presence of clutter and missed detections.

Acoustic line transect surveys are often used in combination with visual methods to estimate the abundance of marine mammal populations. These surveys typically use towed linear hydrophone arrays and estimate the time differences of arrival (TDOAs) of the signal of interest between the pairs of hydrophones. The signal source TDOAs or bearings are then tracked through time to estimate the animal position, often manually.

Automated extraction of dolphin whistles-A sequential Monte Carlo probability hypothesis density approach.

The need for automated methods to detect and extract marine mammal vocalizations from acoustic data has increased in the last few decades due to the increased availability of long-term recording systems. Automated dolphin whistle extraction represents a challenging problem due to the time-varying number of overlapping whistles present in, potentially, noisy recordings. Typical methods utilize image processing techniques or single target tracking, but often result in fragmentation of whistle contours and/or partial whistle detection.

Automated tracking of dolphin whistles using Gaussian mixture probability hypothesis density filters.

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.