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

Bayesian detection and tracking of odontocetes in 3-D from their echolocation clicks

Localization and tracking of marine animals can reveal key insights into their behaviors underwater that would otherwise remain unexplored. A promising nonintrusive approach to obtaining location information of marine animals is to process their bioacoustic signals, which are passively recorded using multiple hydrophones. In this paper, a data processing chain that automatically detects and tracks multiple odontocetes (toothed whales) in three dimensions (3-D) from their echolocation clicks recorded with volumetric hydrophone arrays is proposed. First, the time-difference-of-arrival (TDOA) measurements are extracted with a generalized cross-correlation that whitens the received acoustic signals based on the instrument noise statistics. Subsequently, odontocetes are tracked in the TDOA domain using a graph-based multi-target tracking (MTT) method to reject false TDOA measurements and close gaps of missed detections. The resulting TDOA estimates are then used by another graph-based MTT stage that estimates odontocete tracks in 3-D. The tracking capability of the proposed data processing chain is demonstrated on real acoustic data provided by two volumetric hydrophone arrays that recorded echolocation clicks from Cuvier's beaked whales (Ziphius cavirostris). Simulation results show that the presented MTT method using 3-D can outperform an existing approach that relies on manual annotation.

Automated extraction of baleen whale calls based on the pseudo-Wigner-Ville distribution

Baleen whales produce a wide variety of frequency-modulated calls. Extraction of the time-frequency (TF) structures of these calls forms the basis for many applications, including abundance estimation and species recognition. Typical methods to extract the contours of whale calls from a spectrogram are based on the short-time Fourier transform and are, thus, restricted by a fixed TF resolution. Considering the low-frequency nature of baleen whale calls, this work represents the contours using a pseudo-Wigner-Ville distribution for a higher TF resolution at the cost of introducing cross terms. An adaptive threshold is proposed followed by a modified Gaussian mixture probability hypothesis density filter to extract the contours. Finally, the artificial contours, which are caused by the cross terms, can be removed in post-processing. Simulations were conducted to explore how the signal-to-noise ratio influences the performance of the proposed method. Then, in experiments based on real data, the contours of the calls of three kinds of baleen whales were extracted in a highly accurate manner (with mean deviations of 5.4 and 0.051 Hz from the ground-truth contours at sampling rates of 4000 and 100 Hz, respectively) with a recall of 75% and a precision of 78.5%.

Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles

This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19-24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.

Robust unsupervised Tursiops aduncus whistle-event detection using gammatone multi-channel Savitzky-Golay based whistle enhancement

Detecting whistle events is essential when studying the population density and behavior of cetaceans. After eight months of passive acoustic monitoring in Xiamen, we obtained long calls from two Tursiops aduncus individuals. In this paper, we propose an algorithm with an unbiased gammatone multi-channel Savitzky-Golay for smoothing dynamic continuous background noise and interference from long click trains. The algorithm uses the method of least squares to perform a local polynomial regression on the time-frequency representation of multi-frequency resolution call measurements, which can effectively retain the whistle profiles while filtering out noise and interference. We prove that it is better at separating out whistles and has lower computational complexity than other smoothing methods. In order to further extract whistle features in enhanced spectrograms, we also propose a set of multi-scale and multi-directional moving filter banks for various whistle durations and contour shapes. The final binary adaptive decisions at frame level for whistle events are obtained from the histograms of multi-scale and multi-directional spectrograms. Finally, we explore the entire data set and find that the proposed scheme achieves the highest frame-level F₁-scores when detecting T. aduncus whistles than the baseline schemes, with an improvement of more than 6%.

Multi-target 2D tracking method for singing humpback whales using vector sensors

Acoustic vector sensors allow estimating the direction of travel of an acoustic wave at a single point by measuring both acoustic pressure and particle motion on orthogonal axes. In a two-dimensional plane, the location of an acoustic source can thus be determined by triangulation using the estimated azimuths from at least two vector sensors. However, when tracking multiple acoustic sources simultaneously, it becomes challenging to identify and link sequences of azimuthal measurements between sensors to their respective sources. This work illustrates how two-dimensional vector sensors, deployed off the coast of western Maui, can be used to generate azimuthal tracks from individual humpback whales singing simultaneously. Incorporating acoustic transport velocity estimates into the processing generates high-quality azimuthal tracks that can be linked between sensors by cross-correlating features of their respective azigrams, a particular time-frequency representation of sound directionality. Once the correct azimuthal track associations have been made between instruments, subsequent localization and tracking in latitude and longitude of simultaneous whales can be achieved using a minimum of two vector sensors. Two-dimensional tracks and positional uncertainties of six singing whales are presented, along with swimming speed estimates derived from a high-quality track.

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. The process of estimating TDOAs from data and tracking them through time can be especially challenging in the presence of multiple acoustically active sources, missed detections, and clutter (false TDOAs). This study proposes a multi-target tracking method to automate TDOA tracking. The problem formulation is based on the Gaussian mixture probability hypothesis density filter and includes multiple sources, source appearance and disappearance, missed detections, and false alarms. It is shown that by using an extended measurement model and combining measurements from broadband echolocation clicks and narrowband whistles, more information can be extracted from the acoustic encounters. The method is demonstrated on false killer whale (Pseudorca crassidens) recordings from Hawaiian waters.

Introduction to the special issue on machine learning in acoustics

The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information about events observed in acoustic data. Acoustic data provide scientific and engineering insight ranging from biology and communications to ocean and Earth science. This special issue included 61 papers, illustrating the very diverse applications of ML in acoustics.