Acoustic Source Identification Research Articles

Identification of Acoustic Sources on Antares Launch Pad Using Microphone Phased Array

A microphone phased array (MPA) was mounted in the vicinity of pad 0A of Mid-Atlantic Regional Spaceport located on NASA’s Wallops Flight Facility to identify the evolution of acoustic sources from hold-down to an elevation of ∼200 ft during the launch of an Antares rocket (Northrop Grumman [NG]-19). The MPA was equipped with 70 piezo-resistive dynamic pressure sensors, a visible-band and an infrared-band camera, and a two-axis accelerometer. The noise map demonstrated an effective use of the water injection system to attenuate noise from plume impingement on the pad. They also pointed out the outlet of the exhaust duct, through which the steam and plume mixture ejected out, to be the primary noise source during a significant interval of time. A comparison with prior such measurements during the maiden launch of the Antares vehicle in 2013 showed significant attenuation of noise sources during the NG-19 launch. A strong source caused by the impingement of plume on the pad surface during the 2013 launch was found to be attenuated in 2023. The results showed the utility of an MPA in quantifying the impact of changes made to a launch pad.

Cross-spectrum method for acoustic source identification and visualization of airfoil noise

The identification and visualization of airfoil noise sources are critical for comprehending the interplay between flow and acoustics, and for understanding the generation and propagation of sound. In this paper, we introduce an innovative method, specifically designed to identify and visualize flow-induced noise sources associated with airfoil noise. This approach employs cross power spectral density analysis to distinctly identify flow-induced noise sources. The data for our study comes from large-eddy simulations of a NACA 0012 airfoil, characterized by a Reynolds number of 4×105, a Mach number of 0.058, and an angle of attack of 6.25∘. Different cross-spectrum formulations are examined and evaluated. The analysis includes a comparison of the cross-spectrum method's strengths and limitations relative to established data-driven approaches like dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD). We find that the cross-spectrum method provides both spectral magnitudes and phase topologies, allowing it to effectively compare sound intensities among various sources at specific frequencies, while retaining high-resolution spatiotemporal coherent flow and acoustic dynamics. One of the notable advantages of the cross-spectrum method over DMD or SPOD modes is its lesser reliance on extensive data manipulation in the form of large-size data matrix, making it a more efficient and user-friendly approach for practitioners, particularly when dealing with complex systems or high-dimensional datasets. This characteristic enhances its practicality and accessibility in the field of flow and acoustic visualization. Finally, the newly developed method is applied to three distinct flow transition scenarios to evaluate its proficiency in distinguishing acoustic generation and propagation mechanisms, depending on the specific transition case.

Noise reduction of axial piston motors using multi-objective optimization of reinforcing ribs based on identification of acoustic sources

The optimization of the housing for reducing vibration and noise in axial piston motors is facing challenges related to inaccurate locations of acoustic sources and inappropriate optimization parameters. In this study, a multi-objective optimization of the thicknesses of the reinforcing ribs for reducing the noise of axial piston motors is investigated by using the compression sensing (CS) method and response surface methodology (RSM). Combined with the CS method, the high-resolution reconstruction of sound-intensity images is used to guide the structural optimization areas through locations of acoustic sources. To reduce vibration and noise, the variable rib structure model (VRSM) is established in structural optimization areas. The thicknesses of reinforcing ribs in different areas are considered optimization variables, while the motor housing's peak vibration acceleration obtained by coupling the motor's dynamic model with the hydraulic model is utilized as the objective function. The mathematical relationship between these variables and the objective function is established through the RSM. Utilizing these functions, a multi-objective optimization model is formulated for discretizing the thicknesses of reinforcing ribs to reduce noise, employing a multi-objective genetic algorithm (MOGA) for optimization calculations. Finally, comparing the sound pressure levels of the axial piston motor with and without the optimized reinforcing ribs, the experimental results demonstrate a notable reduction in noise with the optimized reinforcing ribs. Specifically, there's a reduction of 2.7 dB at the peak frequency of 610 Hz.

Off-grid deconvolution beamforming for acoustic source identification

Deconvolution beamforming treats the output of conventional beamforming as the convolution of source strength distribution and point spread function (PSF), and solves it through different algorithms to eliminate the influence of PSF and obtain clear acoustic source identification imaging results. Accurate construction of the PSF requires not only the transfer vectors between the scanning points and the array microphones, but also between the acoustic sources and the array microphones. In practical applications, since the location of sources are unknown, the existing deconvolution beamforming methods assume that the sources lie on the scanning points. They replace the transfer vectors between the sources and the array microphones with those between the scanning points and the array microphones to construct an approximate PSF. When the acoustic sources lie off the scanning points, the source identification performance of existing deconvolution beamforming methods will degrade. To overcome this problem, this paper proposes an off-grid deconvolution beamforming named as Newtonized orthogonal matching pursuit deconvolution approach for the mapping of acoustic sources (NOMP-DAMAS). The method constructs a PSF with the acoustic source coordinates as variables, and constructs the acoustic source identification problem based on deconvolution beamforming as a maximum likelihood estimation with the acoustic source coordinates and averaged sound pressure contribution as variables. It is solved based on NOMP and continuously updates the PSF through iteration to achieve accurate removal of the PSF, resulting in clear imaging and precise acoustic source identification. Simulations and experiments show that the proposed NOMP-DAMAS has good adaptability to sources lie off the scanning points, can accurately locate the sources and accurately quantify their sound pressure contribution, achieving high-efficiency and high-precision acoustic source identification. Besides, its performance is less affected by grid spacing, noise interference, and source coherence, and the acoustic source identification performance is stable.

Grid-free compressive beamforming for arbitrary linear microphone arrays

Grid-free compressive beamforming is a new attractive method for acoustic source identification based on compressive sensing theory, which extracts the source information from the microphone array measurements without discretizing the target source region. In the framework of linear microphone array measurements, many studies have been carried out around this method. They have strict requirements on the distribution of microphones. Specifically, only the uniform arrays with equally spaced microphones and sparse arrays consisting of a selection of microphones from uniform arrays are available. In this paper, based on the prolate spheroidal wave functions, we propose a grid-free compressive beamforming method compatible with arbitrary linear microphone arrays. Some examples are conducted to demonstrate the correctness and superiority of the proposed method. Plenty of Monte Carlo simulations are performed to reveal the effects of source coherence, source separation, noise, and number of snapshots. This study helps to promote the popularization and application of grid-free compressive beamforming method and offers the possibility to improve the acoustic source identification performance by optimizing microphone distribution.

Source contribution analysis of vehicle pass-by noise using a moving multi-band model based OPAX method

The conventional operational path analysis with eXogeneous inputs (OPAX) method has been widely used to investigate the vibration transfer paths. However, the single degree of freedom model is not available for the load identification of acoustic sources due to their unknown source behavior. And the alternative multi-band (MB) model can only offer a deterministic estimation to the load parameters. To assess the acoustic source contribution of vehicle pass-by noise (PBN), this paper develops the OPAX method by introducing a moving multi-band model (MMB). In this method, the vehicle acoustic sources are simplified as a combination of a series of point sources. The source loads are firstly parametrically modelled by MB models. Then the load parameters are solved several times in the MMB and estimated by the index of coefficient of variation, which suppresses the effect of nonlinear random noise and improves the load identification accuracy of acoustic sources. Finally, the source contributions to PBN at receivers can be derived. Both numerical studies and experimental validations are implemented to examine the performance of the proposed method. Results show that compared to the conventional OPAX method, the proposed method can provide a higher accuracy for the source contribution analysis of PBN.

Identification of acoustic point sources in a two-layered medium from multi-frequency sparse far field patterns

We consider the reconstruction of point sources in a two layered medium from the multi-frequency sparse far field patterns taken on the upper half sphere. The point sources are located in both the upper half space and the lower half space, and consequently bring difficulty for the inverse problem because of the combination of two different types of far field patterns. After establishing the uniqueness of the point sources by the multi-frequency far field patterns at properly chosen sparse observation directions, we introduce a multi-step numerical scheme for identifying all the points sources. Numerical examples show that the proposed sampling methods work very well for locating the positions and the formulas for determining the corresponding scattering strengths are valid and stable with respect to the noises.

Super-resolution compressive spherical beamforming based on off-grid sparse Bayesian inference

Compressive spherical beamforming (CSB) with spherical microphone arrays is a panoramic acoustic source identification technology with high spatial resolution and clear acoustic imaging, which has broad application prospects. Due to the discretization of the focus region and the assumption of on-grid sources, classical CSB suffers from the basis mismatch problem, i.e., it faces performance deterioration when identifying off-grid sources. To overcome the problem, this paper proposes off-grid sparse Bayesian inference-based CSB (OGSBI-CSB). OGSBI-CSB formulates the direction of arrival (DOA) as the sum of the DOA of grid point and DOA offset, constructs an off-grid model based on Taylor expansion, and adapts OGSBI to solve the model and obtains DOA and strength estimation. Simulations and experiments demonstrate that the proposed OGSBI-CSB not only can effectively alleviate the basis mismatch problem and then improve identification accuracy for off-grid sources, but also enjoys super-resolution and good resistance to noise interference.

Modified Wideband Acoustical Holography for Improving the Identification of Acoustic Sources at Low Frequencies

Source identification and sound field reconstruction using near-field acoustical holography (NAH) is particularly challenging when the measurement data are relatively fewer than the sources to reconstruct. Fast wideband acoustical holography (WBH) is an inverse solution method based on the compressed sensing theory, which enables the NAH with such sparse measurement and extends its applicable frequency range. WBH usually works well at high frequencies, but it is often unsatisfactory when dealing with low-frequency coherent sources. This work proposes a modified WBH method to improve low-frequency and high-frequency performance substantially. It is developed by combining the conventional WBH and the steepest descent methods in computing the initial source strength values. It contrasts the WBH method using specific initial values for the equivalent sources. The iteration is repeated to ensure that these source strengths are not involved in the threshold filtering process during the iteration process. The simulation and experiment show that the proposed modified WBH substantially improves the reconstruction accuracy of coherent sound sources at low frequencies without degrading the reconstruction performance at mid to high-frequencies.

A Survey on Artificial Intelligence-Based Acoustic Source Identification

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Optimal Sensor Positioning for the Sparse Measurement in the Acoustic Reconstruction of a Large Source

Near-field acoustic holography (NAH) has become an effective tool for acoustic source identification. It usually requires many microphones near the source surface to catch sufficient evanescent waves for good spatial resolution. Besides, such a measurement is time-consuming and uneconomical in practice; standard measurements using densely and uniformly populated sensors are sometimes impossible when dealing with massive sources. Eventually, the compressive sensing (CS) technique has been suggested for NAH, which employs a sparse number of sensors. Most existing works in NAH based on compressive sensing theory have focused on developing acoustic models and algorithms; In contrast, less attention has been paid to the best practice of sensor placement. The present study aims to propose and validate the methods for the optimal selection of sparse measuring points using the compression technique for NAH. Due to the small number of measuring sensors, the transfer matrix between source and hologram points becomes underdetermined. Under such a data condition, even though every piece of data would be felt precious for the reconstruction, the information from some sensors should be discarded further because not all data provide meaningfully independent information. To this end, effective independence and singular-value monitoring methods are used to test the selection of the optimal sensor position from a set of candidate positions. The former chooses non-redundant measurement data, whereas the latter keeps the low condition number of the transfer matrix. Simulation results with vibrating plates show that optimized sensor positions could effectively facilitate the optimality of the approximate solution for CS theory compared to uniformly or randomly distributed measurements. It is because the established transfer matrix has the lowest condition number due to being sparser and more independent sensor positions. Experimental results exhibit a better reconstruction error (10–40%) at 200–2,000 Hz when the sensor positions are optimized than the uniform or random selection approaches.

A Correlation Method for Acoustic Sources Identification Using Multiple-Microphone Measurements

A Correlation Method for Acoustic Sources Identification Using Multiple-Microphone Measurements

Identifying the Acoustic Source via MFF-ResNet with Low Sample Complexity

Acoustic signal classification plays a central role in acoustic source identification. In practical applications, however, varieties of training data are typically inadequate, which leads to a low sample complexity. Applying classical deep learning methods to identify acoustic signals involves a large number of parameters in the classification model, which calls for great sample complexity. Therefore, low sample complexity modeling is one of the most important issues related to the performance of the acoustic signal classification. In this study, the authors propose a novel data fusion model named MFF-ResNet, in which manual design features and deep representation of log-Mel spectrogram features are fused with bi-level attention. The proposed approach involves an amount of prior human knowledge as implicit regularization, thus leading to an interpretable and low sample complexity model of the acoustic signal classification. The experimental results suggested that MFF-ResNet is capable of accurate acoustic signal classification with fewer training samples.

Two-dimensional sparse Bayesian learning compressive beamforming with planar microphone array for acoustic source identification

Two-dimensional (2D) compressive beamforming (CB) with planar microphone array for acoustic source identification has attracted much attention due to its advantages of wide identification space, applicability to both coherent and incoherent acoustic sources, and clear imaging. It realizes acoustic source identification by establishing and solving the underdetermined equations between the sound pressure measured by the microphone array and the source strengths of the discretized grid points in the target area. Convex relaxation algorithms and greedy algorithms are often used to solve these underdetermined equations. CB based on convex relaxation requires the sensing matrix to satisfy the restricted isometry property and needs large calculating consumption, which is unsuitable for large-scale problems. CB based on greedy algorithms may achieve a locally optimal solution instead of the global optimum, and it is easily affected by the coherence of the sensing matrix columns, so its performance is sensitive to the grid spacing. To take into account both the robustness of acoustic source identification performance and high computational efficiency, we adapted sparse Bayesian learning (SBL) to solve the above underdetermined equations and proposed a 2D-SBL-CB in this paper. It first assumes the signal prior and noise prior to derive the posterior probability distribution of the source strength, and then adaptively estimates the hyperparameters of the sparse prior of source strength based on a type-II maximum likelihood, i.e., by maximizing the evidence, to achieve source identification. Both simulations and experiment show that the proposed 2D-SBL-CB is robust to grid spacing and has stable source identification performance. It also enjoys high computational efficiency, strong spatial resolution ability, and strong anti-noise interference, with optimal overall performance.

Improvement of orthogonal matching pursuit deconvolution beamforming method for acoustic source identification

The deconvolution approach for the mapping of acoustic sources (DAMAS) based on orthogonal matching pursuit (OMP-DAMAS) has attracted much attention due to its advantages of high spatial resolution and excellent capability to suppress spurious sources. In this paper, we propose an improved version of OMP-DAMAS based on fast Fourier transformation (FFT), abbreviated as FFT-OMP-DAMAS. This method assumes that the array point spread functions (PSFs) are spatially shift-invariant. The sum of the product of the acoustic source distribution and PSFs is converted into a convolution form, which is further converted into a product in the wave number domain. With these conversions, FFT can be used to improve the solving speed. Both simulations and experiments show that the proposed method not only inherits the advantages of OMP-DAMAS in terms of spatial resolution and spurious source suppression but also significantly improves the computational efficiency compared with OMP-DAMAS.

Acoustic emission sources localization and identification of complex metallic structures based on nearfield frequency space sparse decomposition

Acoustic emission sources localization and identification of complex metallic structures based on nearfield frequency space sparse decomposition

Newtonized orthogonal matching pursuit-based compressive spherical beamforming in spherical harmonic domain

Compressive spherical beamforming (CSB) with spherical microphone arrays is a promising approach for acoustic source identification due to its high spatial resolution and applicability to incoherent and coherent sources. However, the original CSB based on fixed discrete grids suffers from basis mismatch problem. The grid-free CSB requires a priori signal-to-noise ratio (SNR), bears heavy computational load at high frequencies and is subject to a limited number of the sources that can be simultaneously identified at low frequencies. This paper extends Newtonized orthogonal matching pursuit (NOMP) algorithm to solve underdetermined equations of CSB with spherical microphone arrays, and proposes NOMP-based CSB (NOMP-CSB). The proposed approach establishes a maximum likelihood estimation (MLE) model, with direction of arrivals (DOAs) and source strengths as unknown parameters to be solved, in spherical harmonic domain. NOMP-CSB first selects the best candidate from discrete grids and then performs Newton optimization in the local continuous region near the selected grid to iteratively solve the MLE model, thereby achieving DOA estimation and source strength quantification. Besides, NOMP-CSB uses the variations of sound pressure residual to adaptively control iterations, avoiding the estimation of SNR and the number of sources. Numerical simulations and experiments demonstrate that NOMP-CSB can effectively overcome basis mismatch problem and enjoys high source identification accuracy, computational efficiency, spatial resolution, and accuracy of estimating the number of sources. Furthermore, this paper develops NOMP-CSB with multiple-frequency synchronous processing, that is, multiple-frequency synchronous NOMP-CSB (MF-NOMP-CSB), which jointly uses the measured sound pressures at multiple frequencies to synchronously estimate DOAs and the strengths of all frequencies. MF-NOMP-CSB outperforms NOMP-CSB under single-snapshot measurement, and can improve the performance of identifying unstable wideband sources.

Damage identification of wind turbine blades using the microphone array under different parametric and measuring conditions: A prototype study with laboratory-scale models

Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and efficiency of wind energy generation, and it is a challenging issue due to the complicated structures and variational operating conditions. In this investigation, a SHM method for wind turbine blades based on the microphone array and acoustic source identification is proposed. With the equipment of loudspeakers in blade cavities, damage-related information is excited to be captured by the array. To generate accurate acoustic maps with high spatial resolutions, a novel algorithm for sparsity-based sound field reconstruction is developed based on the generalized minimax-concave penalty function. With a laboratory-scale wind turbine model, damage identification performance of the proposed method is evaluated under different parametric and measuring conditions, and experiments are conducted under diverse blade health conditions. Results reveal that and both internal and external damage in operating blades can be recognized as acoustic sources, and satisfactory performance of the proposed method can be guaranteed with appropriate parameters. Furthermore, determination criteria for parameters are concluded with respect to the variation of measuring conditions. This prototype study provides useful insights into the development of effective SHM systems.

Performance Enhancement of Functional Delay and Sum Beamforming for Spherical Microphone Arrays

Functional delay and sum (FDAS) beamforming for spherical microphone arrays can achieve 360° panoramic acoustic source identification, thus having broad application prospects for identifying interior noise sources. However, its acoustic imaging suffers from severe sidelobe contamination under a low signal-to-noise ratio (SNR), which deteriorates the sound source identification performance. In order to overcome this issue, the cross-spectral matrix (CSM) of the measured sound pressure signal is reconstructed with diagonal reconstruction (DRec), robust principal component analysis (RPCA), and probabilistic factor analysis (PFA). Correspondingly, three enhanced FDAS methods, namely EFDAS-DRec, EFDAS-RPCA, and EFDAS-PFA, are established. Simulations show that the three methods can significantly enhance the sound source identification performance of FDAS under low SNRs. Compared with FDAS at SNR = 0 dB and the number of snapshots = 1000, the average maximum sidelobe levels of EFDAS-DRec, EFDAS-RPCA, and EFDAS-PFA are reduced by 6.4 dB, 21.6 dB, and 53.1 dB, respectively, and the mainlobes of sound sources are shrunk by 43.5%, 69.0%, and 80.0%, respectively. Moreover, when the number of snapshots is sufficient, the three EFDAS methods can improve both the quantification accuracy and the weak source localization capability. Among the three EFDAS methods, EFDAS-DRec has the highest quantification accuracy, and EFDAS-PFA has the best localization ability for weak sources. The effectiveness of the established methods and the correctness of the simulation conclusions are verified by the acoustic source identification experiment in an ordinary room, and the findings provide a more advanced test and analysis tool for noise source identification in low-SNR cabin environments.

Acoustic source identification using an off-grid and sparsity-based method for sound field reconstruction

Acoustic source identification based on the microphone array is an attractive technique, and the effective identification of off-grid sources is still a challenging issue. To improve the accuracy and efficiency of source identification, a novel method based on the off-grid model and group sparsity is proposed. Specifically, both source locations and strengths are considered in an integrated model to reconstruct the sound field with the microphone array at arbitrary positions, and the group minimax concave penalty is employed to promote the group sparsity of identification results. Then, the performance of the proposed method is examined with numerical simulations. Results indicate that the proposed method can identify off-grid sources with higher accuracy than existing methods within a broad range of noise levels and source frequencies, and less computing time is needed than using on-grid approaches. Benefit from the capacity to approximate propagation functions, the computational efficiency can be further enhanced by utilizing coarser search grids. Moreover, experimental results demonstrate the satisfactory performance and application potential of the proposed method.