Autonomous acoustic recorders are an increasingly popular method for low-disturbance, large-scale monitoring of sound-producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time-synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time-synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor-intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open-source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low-disturbance, large-scale collection of animal position data.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.6216

Acoustic localization of terrestrial wildlife: Current practices and future opportunities.

A one-bit joint sparse representation direction of arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization allow significant reduction of data to be transmitted to the fusion center without additional energy consumption requirement in the source coding/compression operation. Compared with existing compressive sensing-based DOA estimation methods, the superiority of the proposed approach in providing data volume reduction and performance improvement is verified by both simulations and field experiments using a prototype wireless sensor array network platform.

DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation

We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), and field experiments using a Voxnet system validate the proposed method and demonstrate a significant performance enhancement at a low computation burden.

Low-Complexity 2D Direction-of-Arrival Estimation for Acoustic Sensor Arrays

A compressive sensing joint sparse representation direction of arrival estimation (CSJSR-DoA) approach is proposed for wireless sensor array networks (WSAN). By exploiting the joint spatial and spectral correlations of acoustic sensor array data, the CSJSR-DoA approach provides reliable DoA estimation using randomly-sampled acoustic sensor data. Since random sampling is performed at remote sensor arrays, less data need to be transmitted over lossy wireless channels to the fusion center (FC), and the expensive source coding operation at sensor nodes can be avoided. To investigate the spatial sparsity, an upper bound of the coherence of incoming sensor signals is derived assuming a linear sensor array configuration. This bound provides a theoretical constraint on the angular separation of acoustic sources to ensure the spatial sparsity of the received acoustic sensor array signals. The Cram e ´ r–Rao bound of the CSJSR-DoA estimator that quantifies the theoretical DoA estimation performance is also derived. The potential performance of the CSJSR-DoA approach is validated using both simulations and field experiments on a prototype WSAN platform. Compared to existing compressive sensing-based DoA estimation methods, the CSJSR-DoA approach shows significant performance improvement.

https://www.mdpi.com/1424-8220/16/5/686/pdf

Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

This paper introduces our several preliminary approaches toward understanding temporal soundspace partitioning in bird communities as a self-organizing phenomenon based on behavioral plasticity. First, we describe this phenomenon from our recordings, and show there are asymmetric relationships and the diversity in the temporal avoidance behaviors among the species, using transfer entropy analysis. Then, we consider the evolutionary significance of such a diversity using a computational experiment of the coevolution of the temporal overlap avoidance of singing behaviors among sympatric species with different species-specific song lengths, implying that diversity in the behavioral plasticity in bird communities can contribute to the more efficient establishment of the soundspace partitioning. Finally, we introduce our preliminary works on extracting the temporal dynamics of interaction processes among multiple birds from recordings with a microphone array using an open-source software system for robot audition called HARK.

Complex systems approaches to temporal soundspace partitioning in bird communities as a self-organizing phenomenon based on behavioral plasticity

In this paper, we present simulations and experimentally collected bird song data collected using a modified Voxnet acoustic array node (with four microphones) to perform 3D direction-of-arrival (DOA) estimation of various bird sources. We used the Approximate Maximum-Likelihood (AML) algorithm to construct the steering matrix in the beamforming process for the estimation of the DOA of the bird signals. While the computational burden is high in the 3D scenario, various strategies have been developed to reduce the computational burden of the algorithm for potential real-time applications. Extensive simulations and experimentally collected data are used to validate the effectiveness of the AML algorithm for 3D estimations and the usefulness of the modified Voxnet node. Both the estimated azimuths and elevations have approximately plus and minus 10 degrees of errors.

Fast 3D AML-Based Bird Song Estimation

In this paper, we present simulation and experimental studies of multiple bird source separation based on the Voxnet acoustic array node. The Approximate Maximum Likelihood (AML) method is used to estimate blindly the direction-of-arrivals(DOAs) of the sources to generate the steering vectors in order to separate the sources via beamforming. Simulation and measured data confirmed the proper operations of the AML beamforming algorithm and the Voxnet hardware node.

Poster abstract: voxnet acoustic array for multiple bird source separation by beamforming using measured data

Tracking initiation issue under bearing-only sensor networks is studied A mechanism for tracking initiation based on probability is proposed, aiming at solving problem under measuring environment with false alarm and undetected The mechanism is achieved by two phases including target localization and target confirm In target localization phase, Multiple Target Probability Localization algorithm is proposed to estimate target initial state, providing new thinking for multi-target localization under bearing-only measurements which distinguishes the true target localizations from the false ones relatively by probability In target confirm phase, target probability is introduced as judgment for tracking initiation and a recursive algorithm under multi-sensor is provided The simulation shows that the proposed mechanism can be well-work under different settings and the required computation is limited which is suitable for WSN

Integrated Tracking Initiation Mechanism Based on Probability for Bearing-Only Sensor Networks

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Fast 3D AML-Based Bird Song Estimation

Poster abstract: voxnet acoustic array for multiple bird source separation by beamforming using measured data

Integrated Tracking Initiation Mechanism Based on Probability for Bearing-Only Sensor Networks