Acoustic Source Tracking Research Articles

Identifying Acoustic Wave Sources on the Sun. II. Improved Filter Techniques for Source Wavefield Seismology

In this paper, we refine a previously developed acoustic source filter, improving its reliability and extending its capabilities. We demonstrate how to fine-tune the filter to meet observational constraints and focus on specific wave-front speeds. This refinement enables discrimination of acoustic source depths and tracking of local source wave fronts, thereby facilitating ultralocal helioseismology. By utilizing the photospheric Doppler signal from a subsurface source in a MURaM simulation, we demonstrate that robust ultralocal three-dimensional helioseismic inversions for the granular flows and the local sound speed to depths of at least 80 km below the photosphere are possible. The capabilities of the National Science Foundation’s new Daniel K. Inouye Solar Telescope will enable such measurements of the real Sun.

A novel quaternion linear matrix equation solver through zeroing neural networks with applications to acoustic source tracking

<abstract><p>Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly.</p></abstract>

A vehicle whistle database for evaluation of outdoor acoustic source localization and tracking using an intermediate-sized microphone array

Acoustic source localization and tracking (ASLT) are always hot topics for their wide applications, such as binaural hearing aids, smart devices, and audio–video conference systems. Numerous ASLT algorithms have already been proposed, however only very limited open databases were available for evaluation their performance. To date, most of these existing databases were recorded indoors using only a limited number of microphones. To the best of our knowledge, it still lacks of available open databases to evaluate ASLT algorithms for outdoor applications using an intermediate-sized microphone array. This paper describes a new database for evaluation ASLT algorithms, where a planar array with 32 MEMS microphones were designed to record a great number of vehicle whistles in outdoor environments with the vertical distance about 8 m away from the center of this array to the road surface. Besides the detailed description of this database, an example using the steered response power-phase transform sound source localization method was provided to show that the vehicle whistle database has been successfully built. The vehicle whistle database and the demo code have been uploaded and available online.

Robust processing for multipath in acoustic navigation, tomography, and source tracking

Because sound travels along many refracted paths through the ocean, passing through many horizontal sound speed layers, it is often challenging to unravel the received multipath to convert arrival times to ranges (e.g., by assigning them to eigenrays calculated by a ray tracer), particularly when it must be automated. This problem is exacerbated when the source and/or receivers are in motion. Assigning the wrong eigenray to an arrival results in the arrival time being modeled for the wrong acoustic path and the wrong range assigned to that arrival, resulting in degraded navigation, tomography, or localization. We will show how to take advantage of redundant measurements to ensure that we avoid spurious eigenray assignments. We use an initial “robust” optimization process whose cost function is less sensitive to spurious measurements or outliers than processes using least squared errors. Such processes both enable outliers to be identified and produce better estimates (than if spurious measurements were admitted). Better estimates allow the assignment of eigenrays to arrivals to be better constrained, often leading to corrected assignments. This virtuous cycle results in fewer spurious assignments and improved forward modeling for applications like navigation, tomography, and source tracking.

Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks.

A probabilistic data association-based distributed cubature Kalman filter (PDA-DCKF) method is proposed in this paper, whose performance on tracking single moving sound sources in the distributed acoustic sensor network was verified. In this method, the PDA algorithm is first used to sift the observations from neighboring nodes. Then, the sifted observations are fused to update the state vectors in the CKF. Since nodes in a sensor network have different reliabilities, the final tracking result integrates the estimations from the local nodes, which are weighted with the parameters depending on the mean square error of the estimation and the energy of the received signal. The experimental results illustrated that the proposed PDA-DCKF method is superior to the other DCKF methods in tracking sound sources even under severe noise and reverberant conditions.

Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification.

Ice environments pose challenges for conventional underwater acoustic localization techniques due to their multipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), for passive localization and tracking of single moving, on-ice acoustic sources using two underwater acoustic vector sensors. We incorporate ordinal classification as a localization approach and compare the results with other standard methods. We conduct experiments passively recording the acoustic signature of an anthropogenic source on the ice and analyze these data. The results demonstrate that Vision Transformers are a strong contender for tracking moving acoustic sources on ice. Additionally, we show that classification as a localization technique can outperform regression for networks more suited for classification, such as the CNN and ViTs.

Periodic acoustic source tracking using propagation delayed measurements

There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking (BOT) of an aperiodic acoustic source, this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter (CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown. We solve this problem by using the implicit Gauss-Helmert Sensor Model (GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM (CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.

Automated tracking of multiple acoustic sources with towed hydrophone arrays

Line transect surveys often incorporate a towed hydrophone array to detect and localize marine mammals. The animals are typically tracked based on the estimated time difference of arrivals (TDOAs) of their calls between pairs of hydrophones. The estimated TDOAs or bearings are then tracked through time to obtain animal or group positions, a process often performed manually. This process can be especially challenging in the presence of multiple animal groups that are vocalizing simultaneously, but at the same time do not emit signals consistently through time. In addition, the process is hindered by missed detections and false alarms (false TDOAs). Here, an automated approach to TDOA tracking is outlined, based on a multi-target Bayesian framework, that incorporates target appearance, disappearance, missed detections and false alarms. The method is demonstrated on examples of line transect surveys from Western Canada [Norris et al., J. Acoust. Soc. Am.146, 2805 (2019)] and from Hawaii, USA. [In memory of Thomas F. Norris.]

Distributed Acoustic Source Tracking in Noisy and Reverberant Environments With Distributed Microphone Networks

In this paper, an improved distributed unscented Kalman particle filter (DUKPF) is proposed for the problem of tracking a single moving acoustic source in noisy and reverberant environments with distributed microphone networks. The conventional DUKPF employs the unscented Kalman filter (UKF) for its proposal of particle sampling, whereas the UKF incorporates one single observation from a certain localization function, which is vulnerable to noise or reverberation. To alleviate this problem, multiple observations are extracted from the localization function at each node and incorporated into the state update of the UKF via the probability data association (PDA) technique, yielding the PDA-UKF. Next, employing the PDA-UKF for the proposal of particle sampling, the improved DUKPF is further developed. Finally, the improved DUKPF is adapted for the acoustic source tracking problem, and a distributed acoustic source tracking method is presented. Simulation results reveal that the improved DUKPF achieved better tracking performance than the conventional DUKPF in different noisy and reverberant conditions.

\u2018Normal\u2019 hearing thresholds and fundamental auditory grouping processes predict difficulties with speech-in-noise perception

Understanding speech when background noise is present is a critical everyday task that varies widely among people. A key challenge is to understand why some people struggle with speech-in-noise perception, despite having clinically normal hearing. Here, we developed new figure-ground tests that require participants to extract a coherent tone pattern from a stochastic background of tones. These tests dissociated variability in speech-in-noise perception related to mechanisms for detecting static (same-frequency) patterns and those for tracking patterns that change frequency over time. In addition, elevated hearing thresholds that are widely considered to be ‘normal’ explained significant variance in speech-in-noise perception, independent of figure-ground perception. Overall, our results demonstrate that successful speech-in-noise perception is related to audiometric thresholds, fundamental grouping of static acoustic patterns, and tracking of acoustic sources that change in frequency. Crucially, speech-in-noise deficits are better assessed by measuring central (grouping) processes alongside audiometric thresholds.

Introduction to the Issue on Acoustic Source Localization and Tracking in Dynamic Real-Life Scenes

The fourteen papers in this special section focus on acoustic source localization (ASLT). ASLT is a well-studied topic in signal processing, but most traditional methods incorporate simplifying assumptions such as a point source, free-field propagation of the sound wave, static acoustic sources, time-invariant sensor constellations, and simple noise fields. However, these assumptions may be seriously violated in a range of emerging real-life applications. In these applications, the environment is extremely challenging, with spatially distributed sources, reverberation and reflections, complex noise fields, multiple concurrent speakers, interference signals, and time-varying positions of sources and/or sensors. Aside from classic source localization scenarios, present-day application areas include audio recording with mobile devices (e.g. cell phones, action cameras, and robots), video conferencing on the go, and recording for 3D reproduction and virtual reality. Underwater acoustic localization is another application, which is characterized by intricate propagation patterns. This special issue presents recent advances in the development of signal processing methods for localization and tracking of acoustic sources and the associated theory and applications. Addressing the challenges raised by real-life environments, novel methods are introduced representing advances in array processing, speech processing, and the use of data inference tools.

Unattended Acoustic Sensor Systems For Source Detection, Classification, and Tracking

Distributed and decentralized sensing offers a new and promising paradigm for surveillance, reconnaissance, and situation awareness. This paper introduces a new fully decentralized decision-making platform referred to as environmental monitoring station (EMS) for sensor-level detection, classification, and tracking of acoustic airborne sources in national parks. This custom-designed field-programmable gate array-based platform allows for near real-time transient source detection and classification using the 1/3 octave spectral data extracted locally from the streaming acoustic data. Source tracking through successive angle-of-arrival (AoA) estimation is also implemented locally on the EMS system using an array of microphones. The general headings of the sources generated using the AoA history and the source labels are transmitted to a park station via two possible wireless links. Field test results are provided to evaluate the performance of the overall system.

Acoustic source tracking based on adaptive distributed particle filter in distributed microphone networks

Abstract In this paper, an adaptive distributed particle filter (ADPF) is proposed for single acoustic source tracking in distributed microphone networks (DMNs). To deal with spurious effects due to the reverberation and noise, a modified multiple-hypothesis model is first investigated by exploiting the generalized cross-correlation (GCC) function. Based on this model, the time-delay of arrival (TDOA) selection is performed for constituting the local observation. Then the acoustic source tracking is formulated as a Bayesian filtering problem under the assumption on the Langevin dynamic model of the source motion. Next, an adaptive distributed particle filter (ADPF) is presented to solve the Bayesian filtering problem for distributed acoustic source tracking. To improve the tracking performance, in the proposed ADPF, an adaptive and distributed computation method of the optimal proposal function is designed based on the Gaussian approximation, implemented by utilizing a Markov Chain Monte Carlo (MCMC) sampler and a consensus filter. The main advantage of the proposed acoustic source tracking method is the combination of the strength of the modified TDOA multiple-hypothesis model and the ADPF. Both simulation and real-world recording experiment results show that, the proposed ADPF has a relatively good tracking performance under different SNR conditions and reverberation environments.

Special Issue on Acoustic source localization and tracking in dynamic real-life scenes

Special Issue on Acoustic source localization and tracking in dynamic real-life scenes

Special Issue on Acoustic source localization and tracking in dynamic real-life scenes

Special Issue on Acoustic source localization and tracking in dynamic real-life scenes

Compressive sensing methods for passive localization and tracking of undersea acoustic sources

Matched-field processing techniques can achieve localization of undersea acoustic sources in both range and depth when sufficient environmental information is available. Unfortunately, these techniques are sensitive to environmental mismatch and often fail when localizing multiple acoustic sources. This work presents a family of acoustic source-localization techniques that similarly to matched-field processing exploit environmental information for localizing acoustic sources in both range and depth. Unique features of these methods are their explicit use of a sparse representation of the source-localization map and ability to model environmental mismatch. Tools from the areas of compressive sensing and mathematical optimization are leveraged for developing computationally tractable solvers that enable fast processing of high-dimensional source-localization maps. These localization techniques are also extended for tracking multiple acoustic sources. In this case, it is possible to exploit the inherent sparsity of the innovations that occur between consecutive source-localization maps to enhance the localization results at a negligible computational cost. Numerical results on experimental data are shown to illustrate the performance of the proposed methods.

Novel algorithm for acoustic classification of unmanned aircraft systems

Advances in Unmanned Aircraft System (UAS) technologies have made them smaller and more affordable, causing a proliferation in UAS activity. This increase has led to a rise in UAS-related incidents, exposing the need for stricter UAS regulation. Detection, Localization, and Classification (DLC) of UAS are important techniques for UAS regulation. However, DLC can be challenging, since UAS span a variety of shapes, sizes, speeds, propulsion systems, and operational altitudes; they can be hard to detect with radar; and they can operate in radio silence. Research supporting DLC of UAS has led to the development of novel, passive acoustic signal processing algorithms for real-time classification. In particular, data fusion algorithms combining kinetic and acoustic data into features for classification are being developed. Kinetic data are collected using acoustic signal source tracking algorithms, and spectral content is extracted from frequency-domain analysis. Using free-body modeling and Doppler compensation, classification probabilities can then be calculated for a list of known UAS. Initial classification algorithms have been developed and tested in MATLAB using UAS data collected in operational environments. The test results will also be presented.

A Time–Frequency Masking Based Random Finite Set Particle Filtering Method for Multiple Acoustic Source Detection and Tracking

Considering that multiple talkers may appear simultaneously, a time–frequency (TF) masking based random finite set (RFS) particle filtering (PF) method is developed for multiple acoustic source detection and tracking. The time-delay of arrival (TDOA) measurements of multiple sources are extracted by using a time–frequency masking technique, by which each source’s TF bins are clustered and separated in a joint gain-ratio and time-delay histogram. Since a joint detection and tracking problem is considered, both source positions and source numbers are time-varying and need to be estimated. The tracker is built within a RFS Bayesian filtering framework. Essentially, an RFS process is used to characterize the source dynamics that include source appearance/dissappearance and motion trajectories. Latent variables are also introduced to indicate source dynamics and measurement-source associations. Subsequently, a Rao–Blackwellization PF technique is employed so that the source position state can be marginalized and only the latent variables are estimated by using the PF. The main advantage of the proposed approach is that hypothesis-pruning is formulated in a full probabilistic sense. The performance of the proposed approach is demonstrated in real speech recordings as well as in simulated room environments.

A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network

Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence. These local statistics are then fused between neighbor nodes and a consensus filter is applied to achieve a global estimation. In such an architecture, only the state statistics need to be transmitted among the neighbor nodes. Consequently, the communication cost can be reduced. The distributed posterior Cramér–Rao bound is also derived. Simulation results show that the performance of the DUPF tracking approach is similar to that of centralized PF algorithm and significantly better than that of LS algorithms.

Passive Acoustic Source Tracking Using Underwater Distributed Sensors

Passive acoustic source tracking using underwater distributed sensors has been a severe problem because of the complexity of the underwater channel and the limited resources of the sensors. In this paper, we propose an acoustic source tracking algorithm using underwater distributed sensors. According to the waveguide invariant theory, a slope of the interference pattern which is seen in a spectrogram is proportional to range of the acoustic source. The proposed algorithm matches the interference patterns at the distributed sensors and calculates a distance ratio between source and sensors. A locus of the source by the principal of the circle of Apollonius is estimated. The Apollonius circle, however, still keeps the ambiguity against the correct source location. In addition hyperbola equation is introduced into localization algorithm by estimating time difference of arrival between the received signals at the distributed sensors. Finally the cross point of the circle and hyperbola can be estimated as the position of the acoustic source. The proposed algorithm is tested on sea trial data for acoustic source ranges of 400–2,000 m and frequencies from 50 to 750 Hz. The results show that the proposed algorithm successfully estimates the source location within an error bound of 7.3%.