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

Where's Whaledo: A software toolkit for array localization of animal vocalizations

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

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. However, when multiple animals are calling concurrently, automated tracking of individual sources still poses a challenge, and manual methods are typically employed to link together sequences of measurements from a given source. This paper extends the method previously reported by Tenorio-Hallé, Thode, Lammers, Conrad, and Kim [J. Acoust. Soc. Am. 151(1), 126-137 (2022)] by employing and comparing two fully-automated approaches for azimuthal tracking based on the AVS data. One approach is based on random finite set statistics and the other on message passing algorithms, but both approaches utilize the underlying Bayesian statistical framework. The proposed methods are tested on several days of AVS data obtained off the coast of Maui and results show that both approaches successfully and efficiently track multiple singing humpback whales. The proposed methods thus made it possible to develop a fully-automated AVS tracking approach applicable to all species of baleen whales.

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.

Robust unsupervised Tursiops aduncus whistle enhancement based on complete ensembled empirical optimal envelope local mean decomposition with adaptive noise

Whistle enhancement is an essential preprocessing step in studying dolphin behavior and population distributions. We propose a robust unsupervised whistle enhancement scheme based on improved local mean decomposition using adaptive noise estimation and logarithmic spectral amplitude. First, to further mitigate the mode aliasing problem effect in whistle signal decomposition and achieve better spectral separation of modes, we present a complete ensembled empirical optimal envelope local mean decomposition with adaptive noise algorithm. According to the envelope characteristics of the whistle signals, the proposed algorithm optimally and adaptively decomposes the noisy signal into product functions (PFs) with amplitude and frequency modulation. Second, the whistle enhancement framework consists of the improved minima-controlled recursive averaging for adaptive noise estimation, optimally modified log-spectral amplitude for each noisy product function enhancement, and the Hurst index for reconstructing pure whistle signal estimations with the least damaged PFs. Finally, the proposed scheme is applied to a dataset of long calls from two Tursiops aduncus individuals. After constructing the pure whistle dataset, the experimental results show that the proposed scheme performs better than other compared whistle enhancement schemes under different signal-to-noise ratios.

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.