Advanced beamforming techniques for acoustic source localization

In recent years some progresses have been made in acoustic source localization (ASL) in highly anisotropic plates when the plate material properties are not known. Some simplifying assumptions in many of the earlier source localization techniques that the wave propagates along a straight line from the source to the receiving sensor is not correct for many anisotropic plates. Wave energy in anisotropic solids generally propagate along curved lines and forms non-circular wave fronts. As a result, for highly anisotropic solids the acoustic source localization techniques that assume straight-line propagation of waves from the source to the receiver are bound to give erroneous results. In this paper earlier techniques as well as new developments for acoustic source localization in homogeneous isotropic and anisotropic plates are discussed. The newest source localization techniques in anisotropic plates analyze the non-circular shapes of the wave front. Following this analysis, the acoustic source has been successfully localized in some anisotropic plates without knowing the plate properties. Limitations of the current techniques that still exist and require further investigation are discussed.

Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses crosscorrelation to compare the input and the database. It should be noted that if any two of the known locations stored in the database are too close, the cross-correlation method may have difficulties differentiating between signals generated from the neighboring points. To address this, we propose an algorithm which employs the linear predictive coding (LPC) that takes into account the dominant frequencies of a received signal. Using this approach, we show that the proposed algorithm is able to improve LTM's source localization accuracy under a real environment in the context of source localization for a touch interface.

Acoustic source localization in an anisotropic plate without knowing its material properties – A new approach

We consider the theoretical and practical aspects of locating acoustic sources using an array of microphones. A maximum-likelihood (ML) direct localization is obtained when the sound source is near the array, while in the far-field case, we demonstrate the localization via the cross bearing from several widely separated arrays. In the case of multiple sources, an alternating projection procedure is applied to determine the ML estimate of the DOAs from the observed data. The ML estimator is shown to be effective in locating sound sources of various types, for example, vehicle, music, and even white noise. From the theoretical Cramér-Rao bound analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. Much experimentally measured acoustic data was used to verify the proposed algorithms.

Three dimensional passive acoustic spatial identification of nucleations in superheated droplet detectors

This paper presentsanew approach for the three-dimensional (3-D) localization of sound sources. An acoustic camera uses an angular beamforming to measure the Direction of Arrival (Do A) of an incoming signal, to localize the emission source. The acoustic sensor used in this article is the Brüel & Kjaer acoustic camera transformed to operate inabistatic mode. The transformation consists inaplacing of one of the microphones of the acoustic camera outside of its microphone array. This allows simultaneous estimation of the Do Aand the Time Difference of Arrival (TDo A) of the incoming signal(s). Such sensors were not found. The paper proposes emitter localization in range - cross range - elevation coordinates by combining estimates of TDo Aand Do Aand presents the signal processing method for that purpose. The range resolution of 0.2mwas achieved in an experiment. Experimental results were obtained using different emission sources. Adescription of resolution cell limitations is presented. The obtained results show acoustic noise source localization without the pre-metering of the range of the imaging plane, i.e., withoutaneed to use the additional range meter which is notapart of the acoustic camera. The latter is important in tasks of non-destructive testing.

In distributed microphone arrays, each microphone is connected to a separate recording device, which requires compensation for their different clock shifts. In this paper, we propose a new type of distributed arrays, in which the microphones are moving. This mobility introduces innovation that can be used to estimate the positions of both the sound sources and the microphones, as well as compensate for the clock shifts. Estimation is done using extended Kaiman filters with heuristics that improve the convergence. We discuss the proposed method in the context of mobile robotics: each microphone is attached to a controllable mobile robot. In this scenario, the array can be used to localize sound-emitting objects in the robot's environment, while providing information about robots' positions. Results show that the error of estimating robots' positions is as low as 5 cm with the source source localization error of 6 mm and the clock synchronization error of 178 μs, for the case of 9 microphones and 5 sound sources.

This paper presents a microphone array sound source localization system based on deep learning algorithms. Currently, the most popular acoustic source localization algorithms are based on the traditional array signal processing methods. These methods have good localization performance in the ideal acoustic environment. However, the performances degraded significantly in low signal-to-noise ratio (SNR) and strong reverberation environments. To deal with this problem, this study developed a deep neural network (DNN) based system. Unlike the traditional algorithms with poor adaptability to environmental conditions, the proposed system can automatically learn the spatial information of sound sources under various conditions through training a large amount of data. Furthermore, it can fully utilize all the information of the original data without additional feature extraction. A set of experiments are carried out to evaluate the performance of the proposed system in comparison with the generalized cross correlation phase transform (GCC-PHAT) method. Results verify that the DNN based system achieves higher accuracy under low SNR conditions.

Acoustic Source Localization in Composite Plates using sideband peak count – Index Technique

This paper proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the volumetric SRP (V-SRP). As compared to the SRP, by deploying a sparser volumetric grid, the V-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of the location estimates. By appending a fine search step to the V-SRP, its refined version (RV-SRP) improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios demonstrate the benefits of the proposed approaches. Specifically, the RV-SRP is shown to outperform the SRP in accuracy at a computational cost of about ten times lower.

Acoustic emission source localization is the basic function of the application of acoustic emission technology. For complex structures, mathematical analysis positioning algorithms cannot be obtained, and accurate acoustic emission source localization cannot be obtained, which has always been one of the problems in the actual applications of acoustic emission technology. To solve this problem, this paper proposes an acoustic emission source localization method based on deep learning, which can obtain high-precision acoustic emission source positioning without the need for mathematical analysis positioning algorithms. The AI deep learning acoustic emission source localization method adopts the method of meshing, dividing the grid in the measured structure, generating label data at the grid position, using the label data to conduct AI deep learning training to establish a positioning model, and using the trained model to analyze the actual acoustic emission data (non-labeled data) to locate the acoustic emission source. This paper uses a multi-layer perceptron model to train multi-dimensional features (arrival time and amplitude). This method effectively improves the accuracy of acoustic emission source localization. Experimental results show that the positioning accuracy of the designed deep learning model in the test set reaches 99.625%, which is significantly better than the traditional Time Difference of Arrival (TDOA) positioning algorithm. In addition, this paper further verifies the stability and reliability of the model in localization tasks through credibility metrics such as Score, Margin and Entropy. This article provides a new solution for locating acoustic emission sources in complex structures, and lays a theoretical and practical foundation for the future development of non-destructive testing technology.

Passive acoustic source location and tracking implemented by microphone array has been applied broadly to target detection and fault diagnosis etc. The conventional relative acoustic source location methods often obtain poor location results in complicated environments, especially in impulse noise environments. The proposed paper applies blind source separation to shipboard target location and thoroughly analyzed this new location algorithm. The experiments in this paper show that a more stable and accurate location results may be obtained by this new location way.

Acoustic source localization using L-shaped sensor clusters: A review

This paper presents beamforming techniques for source localization on aicraft in flight with a focus on the development at DLR in Germany. Fly-over tests with phased arrays are the only way to localize and analyze the different aerodynamic and engine sources of aircraft in flight. Many of these sources cannot be simulated numerically or in wind-tunnel tests because they they are either unknown or they cannot be resolved properly in model scale. The localization of sound sources on aircraft in flight is performed using large microphone arrays. For the data analysis, the source signals at emission time are reconstructed from the Doppler-shifted microphone data using the measured flight trajectory. Standard beamforming techniques in the frequency domain cannot be applied due transitory nature of the signals, so the data is usually analyzed using a classical beamforming algorithm in the time domain. The spatial resolution and the dynamic range of the source maps can be improved by calculating a deconvolution of the sound source maps with the point spread function of the microphone array. This compensates the imaging properties of the microphone array by eliminating side lobes and aliases. While classical beamfoming yields results that are more qualitative by nature, the deconvolution results can be used to integrate the acoustic power over the different source regions in order to obtain the powers of each source. ranking of the sources. These results can be used to rank the sources, for acoustic trouble shooting, and to assess the potential of noise abatement methods.

A microphone clustering and back propagation (BP) neural network based acoustic source localization method using distributed microphone arrays in an intelligent meeting room is proposed. In the proposed method, a novel clustering algorithm is first used to divide all microphones into several clusters where each one corresponds to a specified BP network. Afterwards, the energy-based cluster selecting scheme is applied to select clusters which are small and close to the source. In each chosen cluster, the time difference of arrival of each microphone pair is estimated, and then all estimated time delays act as input of the corresponding BP network for position estimation. Finally, all estimated positions from the chosen clusters are fused for global position estimation. Only subsets rather than all the microphones are responsible for acoustic source localization, which leads to less computational cost; moreover, the local estimation in each selected cluster can be processed in parallel, which expects to improve the localization speed potentially. Simulation results from comparison with other related localization approaches confirm the validity of the proposed method.