Acoustic Source Localization Research Articles

Pressure ratio and phase difference in a two-microphone system under uncertain outdoor sound propagation conditions

Predictions of outdoor sound propagation in uncertain conditions are a challenging task. Evidence suggests that using more than one receiver can reduce the effect of uncertainties. This paper studies via numerical simulations the effects of uncertainty in the source/receiver geometry and impedance ground condition on the sound pressure ratio recorded using the two-microphone method. A Monte Carlo method is employed to study the effect of uncertainties in the range and ground parameters. The range and frequency are found to be key parameters which control the resultant probability density function for the absolute sound pressure ratio and phase difference. The introduction of small uncertainty only matters if the uncertainty is present in the distance between the source and receiver. Uncertainties in the impedance ground are found to have a negligible effect. The sound pressure ratio is affected by the uncertainty more strongly at a shorter range. These findings pave the way to the development of more robust methods for outdoor acoustic source localisation and identification from two-microphone data.

Multi-unmanned aerial vehicle multi acoustic source localization

This paper addresses a multisource localization problem with multiple unmanned aerial vehicles equipped with appropriate sensors coordinating with each other, wherein the sources are simultaneously emitting identical acoustic signals. Distributed coordinated localization algorithms based on multiple range and direction measurements are presented and performances are evaluated in different practically significant mission scenarios. Non-deterministic polynomial (NP) hardness to determine optimal number of unmanned aerial vehicles for a given mission scenario is discussed. Group coordination, tactical path, and goal replan strategies to enable efficient localization of single and multiple acoustic sources have been designed. The localization algorithm along with coordination strategy is verified in the presence of realistic error conditions through simulation.

Acoustic emission source localization in composite stiffened plate using triangulation method with signal magnitudes and arrival times

This paper introduced the triangulation method using the relations of the signal magnitudes and distances between each sensor and impact location. In order to extract the useful feature having good correlation with these distances, the time of arrival (TOA) data of sensor signal was applied. Using the signal magnitudes and TOA data, the effective features could be derived for estimating the distances between each sensor and impact location. For investigating this suggested algorithm, low-velocity impact experiments were carried out on the composite stiffened plate as a target structure. The acoustic emission signals were captured by the commercial high-speed optical fiber sensing system. From the impact signals on the baseline locations (30 points), the regression model was constructed for estimating the distances of impact location from each sensor on the target area. Then, through the non-baseline signals (20 points), the validations of constructed regression model were performed. As a result, this regression model is able to accurately predict the AE source locations on the non-baseline cases.

Movement-assisted localization from acoustic telemetry data

BackgroundAcoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., “localization”). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays.MethodsIn this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies.ResultsSimulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by >50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models.ConclusionsMovement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

Online acoustic emission source localization in concrete structures using iterative and evolutionary algorithms

This study is motivated by the need to develop an efficient online structural health monitoring (SHM) framework to accurately localize damage induced acoustic emission (AE) sources in concrete structures. Experimental studies are carried out in concrete slabs considering pencil lead break (PLB) as artificial damage source to initialize acoustic emission (AE) waves. A simplified yet robust Iterative Planar Source (IPS) localization algorithm based on arrival time (ToA) is proposed first to identify arbitrarily selected several damage source locations for (1) rectangular (2)circular, and (3) zig-zag distributed AE sensor network arrangements. The results of the proposed localization algorithm are then compared with those obtained from an evolutionary particle-swarm optimization (PSO) algorithm for each sensor network arrangement to assess the performance of the AE source monitoring strategy. It is found that the zig-zag arrangement of the distributed AE sensors is the most efficient sensor network arrangement for damage source localization. The obtained results clearly represent the accuracy and robustness of the proposed online monitoring framework for localizing damage induced acoustic emission sources in concrete structures without extensive manual intervention.

Evaluation of acoustic emission source localization accuracy in concrete structures

Acoustic emission source localization is a promising monitoring technique for concrete structures. However, the accuracy of acoustic emission source localization is influenced by many factors, such as the presence of cracks, which are commonly observed in existing reinforced concrete structures. In this article, the acoustic emission source localization is evaluated using a numerical model with a total number of 11,827,200 independent simulated tests. In this work, the investigated influential factors include the presence of cracks, arrival time picking error, and senor layout. The accuracy of source localization is quantified by the characteristic error defined in this article. Using the proposed wave propagation properties, a relatively stable characteristic error of 150 mm is estimated in the detection zone with the maximum sensor spacing less than 1 m. The evaluation approach and simulated characteristic error are validated experimentally by comparing the 200 manually generated signals using hammer hits on a cracked concrete beam.

In situ wavenumber–frequency modal decomposition of acoustic emissions

A new acoustic sensing capability, consisting of a flexible high-density linear piezoelectric sensor array coupled to a high-bandwidth interrogation device, is developed and applied to in situ wavenumber–frequency modal decomposition of acoustic emissions in plates. An experimental assessment of the capability is undertaken using acoustic emissions generated by a ball-drop impact on aluminium and composite panels. A new method for acoustic source localisation based on this new sensor array is described, and it is shown to be more accurate than a conventional multilateration technique using single-point piezoelectric receivers. The potential of this new capability for acoustic emission detection, location and possible quantification is discussed.

Design and Implementation of Acoustic Source Localization on a Low-Cost IoT Edge Platform

The implementation of algorithms for acoustic source localization on edge platforms for the Internet of Things (IoT) is gaining momentum. Applications based on acoustic monitoring can greatly benefit from efficient implementations of such algorithms, enabling novel services for smart homes and buildings or ambient-assisted living. In this context, this brief proposes extreme low-cost sound source localization system composed of two microphones and the low power microcontroller module ESP32. A Direction-Of-Arrival (DOA) algorithm has been implemented taking into account the specific features of this board, showing excellent performance despite the memory constraints imposed by the platform. We have also adapted off-the-shelf low-cost microphone boards to the input requirements of the ESP32 Analog-to-Digital Converter. The processing has been optimized by leveraging in parallel both cores of the microcontroller to capture and process the audio in real time. Our experiments expose that we can perform real-time localization, with a processing time below 3.3 ms.

Application of the adaptive array reduction method for offset acoustic source localisation

Acoustic beamforming arrays are typically designed assuming that the noise source of interest is aligned with the geometric centre of the array or near it, and with some prior knowledge of the frequency range of interest. This method of array design works well and provides sufficient performance provided that the source of interest is located at or near the centre of the investigated region, known as a scanning grid. If a source is recorded at some distance away from the scanning grid centre, its representation within a beamforming source map can be distorted, the Main Lobe Width (MLW) can be enlarged and the remainder of the source map may possess significant sidelobes. The magnitude of the Maximum Sidelobe Level (MSL) often increases as the source is moved away from the scanning grid centre, which can be problematic when the relative alignment between the array centre and the source cannot be altered. By using an array design that is fixed within a testing facility, the region in which optimal MSL and MLW performance can be obtained is limited. By using a recently published technique known as the Adaptive Array Reduction Method (AARM), these issues can be overcome provided that some information regarding the source location and the frequency range of interest is known prior. An array can be designed for a specific source location and frequency, thus significantly improving the MSL, MLW and lobe-distortion, relative to an array locked in a fixed position. In this paper, the AARM is experimentally verified, revealing excellent agreement with simulated source maps. Furthermore, the AARM is used to design arrays based on a centred and non-centred source and their numerical and experimental performances are compared against some logarithmic spiral and randomised pattern arrays, clearly revealing significantly improved source image maps for acoustic sources located away from the scanning grid centre.

Determination of propagation model matrix in generalized cross-correlation based inverse model for broadband acoustic source localization.

The generalized cross-correlation (GCC) based inverse model is a promising time domain localization technique for a broadband acoustic source. Its performance is affected by the temporal width threshold of the propagation model matrix. The appropriate threshold should vary with focus distance, array geometry, and array size, but there is still a lack of uniform and effective methods to determine it. To solve this issue and perfect the technique, a method is proposed based on the cumulative probability of all differences of time delay estimation between the focus point and the microphone pair. Further, an alternative propagation model matrix is derived, which circumvents the threshold. Both proposed methods are effective under different simulation and experimental configurations, with strong stability and adaptability to focus distance, array geometry, and array size. The GCC based inverse model with either proposed method not only enjoys satisfactory source localization performance, including narrow mainlobes, few spurious sources, and highlighted source positions, but also can correctly estimate the sound level, which is comparable to that with the preset appropriate temporal width threshold. In terms of the computational time, the inverse model with the latter proposed method outperforms that with the former one.

Deconvolution algorithms of phased microphone arrays for the mapping of acoustic sources in an airframe test

Beamforming methods with phased microphone arrays are widely used for the characterization of acoustic sources. Compared with conventional beamforming, deconvolution algorithms, such as DAMAS, NNLS, FISTA, and SpaRSA, can significantly improve the spatial resolution but require huge computational effort. However, there are no experimental applications of FISTA and SpaRSA for complex acoustic sources localization. In this paper, deconvolution algorithms (DAMAS, NNLS, FISTA, and SpaRSA) are compared through experimental applications of benchmark test DLR1, which contains complex sound sources distributions. Results show that DAMAS, NNLS, and FISTA can distinguish dominant acoustic sources for a broad range of frequencies in complex acoustic sources distributions cases. SpaRSA has great possibilities of being invalid in complex aeroacoustic measurements. Besides, DAMAS, NNLS, and FISTA with compression computational grid based on conventional beamforming (denoted as DAMAS-CG2, NNLS-CG2, and FISTA-CG2, respectively) successfully save lots of computational time and retain the spatial resolution. Therefore, DAMAS-CG2, NNLS-CG2, and FISTA-CG2 can be considered as the promising acoustic array data processing methods for complex acoustic sources localization.

Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method

Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods.

Machine listening in spatial acoustic scenes with deep networks in different microphone geometries

Multi-channel acoustic source localization evaluates direction-dependentinter-microphone differences in order to estimate the position of an acousticsource embedded in an interfering sound field. We here investigate a deep neuralnetwork (DNN) approach to source localization that improves on previous workwith learned, linear support-vector-machine localizers. DNNs with depthsbetween 4 and 15 layers were trained to predict azimuth direction of targetspeech in 72 directional bins of width 5 degree, embedded in an isotropic,multi-speech-source noise field. Several system parameters were varied, inparticular number of microphones in the bilateral hearing aid scenario wasset to 2, 4, and 6, respectively.
 Results show that DNNs provide a clear improvement inlocalization performance over a linear classifier reference system.Increasing the number of microphones from 2 to 4 results in a larger increase ofperformance for the DNNs than for the linear system. However, 6 microphonesprovide only a small additional gain. The DNN architectures perform betterwith 4 microphones than the linear approach does with 6 microphones, thusindicating that location-specific information in source-interference scenariosis encoded non-linearly in the sound field.

Research on Acoustic Source Localization Based on Acoustic Holography

Research on Acoustic Source Localization Based on Acoustic Holography

RNN for Solving Time-Variant Generalized Sylvester Equation With Applications to Robots and Acoustic Source Localization

A generalized Sylvester equation is a special formulation containing the Sylvester equation, the Lyapunov equation and the Stein equation, which is often encountered in various fields. However, the time-variant generalized Sylvester equation (TVGSE) is rarely investigated in the existing literature. In this article, we propose a noise-suppressing recurrent neural network (NSRNN) model activated by saturation-allowed functions to solve the TVGSE. For comparison, the existing zeroing neural network (ZNN) models and some improved ZNN models are introduced. Additionally, theoretical analysis on the convergence and robustness of the NSRNN model is given. Furthermore, computer simulations on illustrative examples and applications to robots and acoustic source localization are carried out. Validation results synthesized by the NSRNN model and other ZNN models are provided to illustrate the ability in solving the TVGSE and dealing with noises of the NSRNN model, and the inaction of other ZNN models to noises.

Acoustic emission source localization method for high-speed train bogie

Bogie is one of the most critical parts of high-speed train and is directly related to the operation quality and safety of the train. However, currently no dynamic non-destructive testing method exists for real-time monitoring. Therefore, this paper proposes a new damage localization method for high-speed train bogie dynamic testing. The acoustic emission testing technology is applied to test the vulnerable welding parts of the bogie with time reversal localization method. Firstly, the bogie welding parts structure model based on finite element software is established. Then an acoustic emission damage signal is sent from the model and the acoustic emission source signal is received by the preset acoustic emission sensor. And the accurate localization of damage is achieved by dealing with the received signal according to the time reversal focusing principle and imaging the acoustic emission source. The simulation experiments show that the proposed method can accurately find the damaged localization. And finally, the method has been verified through experiment, and the results show it can effectively improve signal energy of the damage source, and the position of the damage source can be shown accurately through signal reconstruction and localization in the detection area.

Weighted subspace detection method based on modal attenuation law in shallow water

In this paper, the modal space detector (MSD) is investigated in shallow water environment when utilizing a vertical linear array. The processing gain of the MSD is derived, and the result demonstrates that the processing gain of the MSD degrades when the number of the propagated normal modes excited by the underwater acoustic source increases, and therefore the detection performance of the MSD decreases. By exploiting the orthogonality among the modal depth functions, the MSD can be decomposed into a group of modal subspace detectors (MSSDs). The processing gains of these MSSDs are derived as well and it is found out that the processing gain of a MSSD is in direct proportion to its corresponding modal attenuation coefficients. By designing a group of weighting coefficients based on the mode attenuation law, a weighted modal subspace detector (WMSSD) is proposed to alleviate the degradation of the processing gain processing of the MSD. We analyze the influences of acoustic source locations and sound velocity profiles (SVPs) on the detection performance of the WMSSD theoretically, and verify the theoretical analyses by comparing its processing gain with the MSD in simulation experiments. The results show that the WMSSD presents various processing gains versus different acoustic source locations. In the waveguide having a negatively-gradient SVP, there exists a ‘weak detection area’ for the WMSSD, that is, the processing gain of the WMSSD is smaller than that of the MSD when the acoustic source locations are close to sea surface. The reason is because there are inversion points on the lower-order modal depth functions and the depths of the inversion points are close to sea surface. In other most areas, the processing gain of the WMSSD is larger (even remarkably larger) than that of the MSD. In the waveguide having a positively-gradient SVP, due to the phenomenon that the modal inversion points of the lower-order modal depth functions are near sea floor, there is a contrary consequence, that is, the ‘weak detection area’ is close to sea floor. And meanwhile the WMSSD outperforms the MSD in other most areas as well. There are no modal inversion points in the waveguide having a constant SVP, and therefore the WMSSD always outperforms the MSD.

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors’ knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a ten-fold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5”) and 48 mm (1.9”), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5”) and 67 mm (2.1”), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with single-sensor.

Diagonal Unloading Beamforming in the Spherical Harmonic Domain for Acoustic Source Localization in Reverberant Environments

Spherical microphone arrays allow the sound field analysis in three dimensions with the advantage of having the same resolution in all directions. By considering the frequency-independent character of the steering vectors in the spherical harmonic (SH) domain, we propose a very low-complexity SH diagonal unloading (DU) beamforming with a novel frequency smoothing power transform (FSPT) of the covariance matrices. We consider the direction of arrival (DOA) estimation problem of acoustic sources in reverberant conditions. The DU beamforming provides high resolution directional response since it exploits the subspace orthogonality property of the covariance matrix by the removal or the attenuation of the signal subspaces, obtained through the subtraction of an opportune diagonal matrix from the covariance matrix. The FSPT aims at smoothing the narrowband covariance matrices of the entire set of frequency domain components, and it pursues this goal by minimizing the narrowband error contributions due to reverberation in the broadband frequency smoothing covariance matrix. We analyze the DOA estimation performance using speech signals with simulations and real acoustic data in reverberant conditions. The results show that the proposed SH-DU-FSPT has a DOA estimation performance comparable to that of high resolution state-of-the-art methods with a significant reduction of the computational cost, since the steering directional responses are computed on the broadband frequency smoothing covariance matrix.

Gas leakage acoustic source localisation with compressed sensing method in sensor networks

Aiming at solving the problem of the concentration signal hardly being compressive sensed directly in nonlinear gas diffusion environment, a compressed sensing direction of arrival (DOA) estimation method according to the acoustic characteristics of the gas leakage was proposed for source localisation. Firstly, the corresponding compressed sensing matrix and the DOA estimation model was established. Then, the sparse Bayesian recovery algorithm was designed for the DOA estimation of gas leakage acoustic source. Finally, the simulation results show that the proposed method could achieve an accurate DOA estimation of one or more gas leakage acoustic sources, and the sparse Bayesian recovery algorithm could effectively improve the estimation accuracy and robust performance with fewer amounts of samples compared to the orthogonal matching pursuit (OMP) algorithm.