Acoustic Source Localization Research Articles

Acoustic Sensor for Detecting Intruders in Wild Environments

Abstract The aim of this work is to develop a device capable to record multiple audio signals (in our case 4 audio signals from 4 microphones of the area) and transmit the information through a network for acoustic source localization. We briefly discuss the first two versions, then the HRTF (Head Related Transfer Function) version of the acoustic sensors is detailed. Experimental results for identifying sound sources are also presented.

On the Semidefinite Programming Algorithm for Energy-Based Acoustic Source Localization in Sensor Networks

The received energy has been becoming an efficient and attractive measure for acoustic source localization due to its cost saving in both energy and computation capability. We investigated the acoustic source localization problem based on received energy measurements in sensor networks. Focusing on the non-logarithmic energy attenuation model, we developed and compared a suite of semidefinite programming (SDP)-based source localization methods due to computational efficiency and numerical reliability. First, we proposed a general SDP-based estimator by jointly estimating the source location and the source radiation power. It yields an efficient estimate for both the scenario where the source is located inside the convex hull formed by sensors and the scenario where the source is located outside the convex hull. Next, a min–max approximation is given to cope with the applicable application of the existing energy-based source localization algorithms relying on the Gaussian energy noise assumption. Furthermore, a novel norm approximation method is proposed according to norm equivalence, which can provide a comparable performance with lower computational complexity. Simulations show that our proposed methods exhibit a superior performance than the existing energy-based source localization estimators.

TDOA-Based Multiple Acoustic Source Localization Without Association Ambiguity

Multiple source localization using time-differences of arrival (TDOAs) is challenging because of the ambiguity involved in associating the TDOAs computed across microphone pairs to the sources. We show that the association ambiguity of the TDOAs can be effectively resolved using the concept of an inverse delay interval region (IDIR), which we introduce in this paper. By examining the association between a spatial domain and the TDOAs, we define IDIR as an interhyperboloidal spatial region corresponding to an interval of delays for a given pair of microphones. The proposed scheme for localizing multiple sources involves two stages. In the first stage, the given enclosure is partitioned into nonoverlapping elemental regions and the ones that contain a source are detected using a measure based on the generalized cross-correlation with phase transform and the IDIRs. In the second stage, the sources are finely localized within each of the detected elemental regions by identifying the IDIRs containing a single source and a novel region-constrained localization approach. We evaluate the performance of the proposed approach on real recordings from the AV16.3 corpus and in a simulated reverberation setting with a reverberation time RT60 of up to 500 ms, and show that the DOA estimation error with two active speakers is within 2° and the spatial localization error is less than 30 cm for each speaker.

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates.

This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker’s gender, nor on the size of the signal window being used.

Robust Directional Angle Estimation of Underwater Acoustic Sources Using a Marine Vehicle.

Acoustic source localization is used in many underwater applications. Acquiring an accurate directional angle for an acoustic source is crucial for source localization. To achieve this purpose, this paper presents a method for directional angle estimation of underwater acoustic sources using a marine vehicle. It is assumed that the vehicle is equipped with two hydrophones and that the acoustic source transmits a specific signal repeatedly. The proposed method provides a probabilistic model for time delay estimation. The probability is recursively updated by prediction and update steps. The prediction step performs a probability transition using the angular displacement of the marine vehicle. The predicted probability is updated using a generalized cross correlation function with a verification process using entropy measurement. The proposed method can provide a reliable and accurate estimation of the directional angles of underwater acoustic sources. Experimental results demonstrate good performance of the proposed probabilistic directional angle estimation method in both an inland water environment and a harbor environment.

Investigation of acoustic emission source localization performance on the plate structure using piezoelectric fiber composites

Due to its unique structure, piezoelectric fiber composites (PFCs) is receiving more and more interests in the field of structural health monitoring. In this paper, PFCs was fabricated and used as an acoustic emission sensor. The simulated signal was generated by a pencil lead break on an epoxy plate and the localization of source was measured based on acoustic emission technology. The results showed that PFCs exhibited an apparent directional sensitivity owing to its unique electric field and architectural heterogeneity, which was different from the conventional piezoelectric disc and 1–3 composite sensors. When the acoustic emission source was located along the fiber length, the PFC sensor could provide precise location information no matter how long the distance between the source and sensor was. The planar localization performance demonstrated that PFCs presented a higher accuracy in the direction normal to the fiber length than in other directions owing to the directivity of PFCs sensor’s response. These special differences of PFCs could be validated by the sensitivity factors in various directions The high sensitivity ensured PFCs possessed a capability of reliable and precise localization that was comparable to a conventional PZT disc sensor.

Multi-source passive acoustic source localisation

Accurate acoustic source localisation (ASL) has significant potential to improve diagnostic ultrasound imaging of small vessel structures. Recently, super-resolution imaging techniques for both active and passive source mapping have been developed; however, these approaches assume the presence of a single source within the point spread function (PSF) of the system. In reality, multiple sources may be present, for example, when high concentrations of contrast agent are used. We use a two-step approach to localise multiple sources within the PSF of a clinically relevant passive cavitation detection system. First, we apply a Blind Source Separation (BSS) technique known as Independent Component Analysis, which relies on higher-order statistics to separate and reconstruct signals originating from independent sources. Second, we determine the time-difference-of-arrival of the separated signals on each receiver, and perform ASL by fitting a polynomial using least squares regression. Simulation and experimental results demonstrate that with the BSS-ASL combination, multiple sources aligned axially within the PSF of a single array located 70 mm from the focus can be localised with sub-millimetre resolution. We verify sources as distinct using Passive Acoustic Maps generated from a perpendicular linear array. Further work is necessary to ascertain performance limits and to validate the technique in vivo.

Big data based ocean acoustic source localization via deep learning

Machine learning has shown its potential in ocean acoustic source localization when real data are available to construct the training data set (Niu et al., JASA 142, 1176–1188 (2017); Niu et al., JASA 142, EL455–460 (2017)). This paper investigates the cases suffering from deficient real observations, where big data generated from the acoustic propagation model are used as the training set and therefore deep learning models could be exploited. To handle the environmental uncertainty, the multi-frequency acoustic data are generated using an acoustic propagation model (KRAKEN) based on tens of thousands of environments. A 50-layer residual neural network model (ResNet-50) is used to address this challenging problem. The performance on simulated and experimental data demonstrates that the proposed approach is able to adapt to a variety of environments in source localization problems.

Acoustic Emission Source Localization System Using Fiber Bragg Grating Sensors and a Barycentric Coordinate-Based Algorithm

Structures and machines are very susceptible to some barely visible defects; acoustic emission (AE) is an effective technique of detecting defects and examining growth and location of these defects. In this paper, a new method was proposed to predict the position of the AE source. The dynamic strain signal from the AE source was acquired by fiber Bragg grating (FBG) sensors. Complex Morlet wavelet transform was used to extract narrowband signals from AE waves. It was proposed that the barycentric coordinate-based localization algorithm can be used to predict the coordinates of the AE source. The validation tests based on the designed AE detection system were performed on a 500 mm × 500 mm × 2 mm Al-alloy plate. The experimental results show that the proposed method is feasible.

Arrangements of phased microphone arrays for acoustic source localization based on deconvolution algorithms

Nowadays phased microphone arrays have become a standard technique for acoustic source localization. Until now microphone arrangement is generally based on the conventional beamforming. Compared to the conventional beamforming, deconvolution algorithms such as DAMAS can achieve significantly improved spatial resolution. Recently the computing speeds of deconvolution algorithms have become faster and faster due to the developments of acceleration algorithms and computer ability. In this paper, phased microphone arrays based on deconvolution algorithms is preliminarily investigated in this paper. Three common microphone arrangements (circle array, grid array and spiral array) with same microphone channels are designed firstly. Subsequently their dynamic ranges with deconvolution algorithms are compared. At low frequency, these three arrays have nearly the same dynamic range. However at high frequency, circle array nearly has the same dynamic range with spiral array, while grid array has smallest dynamic range. Considering the experimental costs, circle array could replace spiral array as the first choice of microphone arrangement based on deconvolution algorithms

A Research on Acoustic Emission Signal Source Localization Algorithm

A Research on Acoustic Emission Signal Source Localization Algorithm

Acoustic emission source location for composite plate based on empirical wavelet transform

Acoustic emission source location for composite plate based on empirical wavelet transform

Sensitivity-based region selection in the steered response power algorithm

Abstract The steered response power (SRP) algorithm is a well-studied method for acoustic source localization using a microphone array. Recently, different improvements based on the accumulation of all time difference of arrival (TDOA) information have been proposed in order to achieve spatial resolution scalability of the grid search map and reduce the computational cost. However, the TDOA information distribution is not uniform with respect to the search grid, as it depends on the geometry of the array, the sampling frequency, and the spatial resolution. In this paper, we propose a sensitivity-based region selection SRP (R-SRP) algorithm that exploits the nonuniform TDOA information accumulation on the search grid. First, high and low sensitivity regions of the search space are identified using an array sensitivity estimation procedure; then, through the formulation of a peak-to-peak ratio (PPR) measuring the peak energy distribution in the two regions, the source is classified to belong to a high or to a low sensitivity region, and this information is used to design an ad hoc weighting function of the acoustic power map on which the grid search is performed. Simulated and real experiments show that the proposed method improves the localization performance in comparison to the state-of-the-art.

Inverse Scheme for Acoustic Source Localization using Microphone Measurements and Finite Element Simulations

Inverse Scheme for Acoustic Source Localization using Microphone Measurements and Finite Element Simulations

Design of microphone phased arrays for acoustic beamforming

The design of a microphone array affects the performance of beamforming algorithms in the localization and evaluation of acoustic noise sources. This paper addresses a circular phased array design for near-field aeroacoustic measurements in a closed-test-section wind tunnel. Microphones were distributed in rings and occupied an equal aperture area—each ring could have a different number of microphones. The array performance was evaluated through the dynamic range and array resolution given by the beamwidth. The array designed for the exploration of the novel approach had 112 microphones and 950-mm aperture. In comparison with classical designs, also optimized for the same number of microphones and aperture, the approach provided the best array resolution and a high dynamic range level almost uniform over the frequency range of interest (800 to 20,000 Hz). Microphone shading was also assessed for improving the array performance, and the employment of only the outermost microphones (the innermost ones were shaded) reduced approximately 40% the array beamwidth.

Underwater Acoustic Pulsed Source Localization with a Pair of Hydrophones

A series of underwater acoustic localization experiments were conducted in the Eastern Mediterranean Sea to test the performance of a Bayesian method for localization of pulsed acoustic sources exploiting time differences between direct and surface-reflected arrivals at two hydrophones of known depth. The experiments involved a controlled source (pinger) at various depths/ranges as well as vocalizing sperm whales encountered off southern Crete. The localization method provides primarily range and depth information. In addition, if the location of the hydrophones in the horizontal is known, horizontal localization can be performed as well, subject to left–right ambiguity; this was applied for whale localization. The localization results confirmed the anticipated behavior of range, depth, and bearing estimation errors, which, according to theory, depend mainly on the source azimuth. In particular, range and depth estimation errors are larger for source locations close to broadside to the array and smaller towards endfire, and they increase with range. Conversely, bearing estimation errors are larger close to endfire and smaller towards broadside. Localizations in this paper were performed to ranges of about 3.5 km. The limiting factors for localization to longer ranges were the loss of ability to resolve direct and surface-reflected arrivals as well as the self-noise of the hydrophones.

Stochastic matched-field localization of an acoustic source based on principles of Riemannian geometry.

Passive localization of acoustic sources is treated within a geometric framework where non-Euclidean distance measures are computed between a cross-spectral density estimate of received data on a vertical array and a set of stochastic replica steering matrices, rather than traditional replica steering vectors. A processing scheme involving matrix-matrix comparisons where steering matrices, as functions of the replica source coordinates, naturally incorporate environmental variability or uncertainty provides a general framework for considering the acoustic inverse source problem in an ocean waveguide. Within this context a subset of matched-field processors is examined, based on recent advances in the application of non-Euclidean geometry to statistical classification of data feature clusters. The matrices are interpreted abstractly as points in a Riemannian manifold, and an appropriately defined distance measure between pairs of matrices on this manifold defines a matched-field processor for estimating source location. Acoustic simulations are performed for a waveguide comprising both a depth-dependent sound-speed profile perturbed by linear internal gravity waves and a depth-correlated surface noise field, providing an example of the viability of this approach to passive source localization in the presence of sound-speed variability.

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks.

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.

Robust AOA based acoustic source localization method with unreliable measurements

Abstract The performance degradation problem of the angle of arrival (AOA)-based acoustic localization methods in the presence of unreliable bearing measurements (outliers) is addressed in this paper. Two typical M-estimators based on Tukey and Huber functions are applied to tackle the problem. Both functions are solved by the iterative reweighted nonlinear least squares (IRNLS) method. Considering the Huber function is convex in nature, it is specifically utilized to mitigate the influence of large residuals on pseudolinear estimator (PLE) by convex optimization. To make the IRNLS method more feasible to use, an approximate relationship between the outlier probability and the bound parameter is provided. The robustness and effectiveness of the proposed methods are clearly demonstrated through a series of simulation results in the presence of various unreliable measurements.

Acoustic source localization based on the generalized cross-correlation and the generalized mean with few microphones.

Microphone arrays are an efficient tool for localizing acoustic sources. Time domain beamforming is one of the techniques which can be used to build a sound source map. This technique based on the cross-correlation of the microphone signals can be seen as a broadband beamforming method. The spatial likelihood functions are averaged with the arithmetic mean. Recently, it has been shown that the harmonic and geometric means can improve the quality of the sound source map. In this study, the influence of the exponent value of the generalized mean on the sound source map is investigated. Then it is shown that the proposed technique is less sensitive to the number of microphones and therefore, it can be reduced.