Mapping Of Acoustic Sources Research Articles

High-Resolution localization of broadband sound sources in a duct using out-duct array measurements

This paper investigates using an Iterated Bayesian Focusing Algorithm and out-duct array measurements to identify multiple broadband sources placed in a cylindrical duct. The problems of large main lobe and low resolution due to localization of sources using conventional methods are investigated in this paper. To solve these problems, an Iterated Bayesian Focusing (IBF) method is utilized to localize the broadband sources. To evaluate and confirm the efficacy of the method, simulation tests and experimental validation are performed. In our experiments, we used a 56-channel dobby spiral array for out-duct measurements and beamforming method, equivalent source method (ESM), deconvolution approach for the mapping of acoustic sources (DAMAS), and Iterated Bayesian Focusing (IBF) method to localize broadband sources inside the duct. Simulation and experimental results demonstrate that the Iterated Bayesian Focusing (IBF) method significantly reduces the main lobe width and minimizes the number of minor lobes. This leads to improved accuracy in localizing double sources.

Learning to sound imaging by a model-based interpretable network

Acoustic beamforming methods based on microphone arrays have been widely used for sound source localization in various industrial fields. The conventional methods such as Delay and Sum (DAS) beamforming are limited by poor spatial resolution although their computational complexity is low. Efforts have been made to improve the resolution of the beamforming map, achieved by both model-based and deep network-based approaches. However, the model-based methods usually suffer from high additional computational effort and largely rely on selecting user-determined parameters. The deep network-based methods may make it difficult to guarantee generalization as well as hard to generate the beamforming map directly. In this paper, we first propose a scheme DAMAS-FISTA-LASSO to solve the inverse problem of sound imaging, significantly reducing runtime requirements. By deep unfolding the proposed algorithm, we further have designed an end-to-end interpretable model-based deep neural network termed DAMAS-FISTA-LASSO-Net (DFLNet) for real-time and high-resolution mapping of acoustic sources, which could obtain satisfying performance on both simulated and real-world data, demonstrating the strong generalization capability of the network.

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.

Noise characteristic of the nacelle/pylon/slat juncture in a realistic high-lift aircraft configuration

The leading-edge slat is an important contributor to airframe noise. Recent studies revealed that the leading-edge slat in realistic high-lift aircraft configurations forms a more complex nacelle/pylon vortex system, resulting in an intense noise source. Thus, this study investigated the noise characteristics of leading-edge slats in realistic aircraft configurations based on phased microphone arrays. The non-negative L1/2 regularization method for the deconvolution approach for the mapping of acoustic sources (DAMAS) was applied to the airframe noise benchmark test DLR1. An uneven and concentrated source strength distribution around the nacelle/pylon/slat juncture region was obtained owing to the three-dimensional flow pattern. Thus, the leading-edge region was partitioned into small sub-regions to extract the nacelle/pylon/slat juncture noise source. The angle of attack (AoA) has different effects on the slat and nacelle/pylon/slat juncture noises. In detail, the minimum amplitude of the slat noise was obtained at AoA = 5°. In contrast, the amplitude of the nacelle/pylon/slat juncture noise increased as the AoA increased, especially at a high AoAs. The significant increase in the amplitude of the nacelle/pylon/slat juncture noise was mainly attributed to the large increase in the spectrum at the low-frequency range with the increase in the AoA. In addition, the amplitude levels of the slat and nacelle/pylon/slat juncture noises approximately follow the fifth power law of the Mach number with their spectra following a similar scaling law. Both noise spectra followed the Strouhal number scaling laws in the low-frequency range and Helmholtz number scaling law in the high-frequency range. However, both noise sources have different noise generation mechanisms. The revealed noise characteristics are helpful for establishing a physics-based nacelle/pylon/slat juncture noise prediction model and subsequently, the development of noise reduction technologies.

A comparison between aeroacoustic source mapping techniques for the characterisation of wind turbine blade models with microphone arrays

<p class="Abstract">Characterising the aeroacoustic noise sources generated by a rotating wind turbine blade provides useful information for tackling noise reduction of this mechanical system. In this context, microphone array measurements and acoustic source mapping techniques are powerful tools for the identification of aeroacoustic noise sources. This paper discusses a series of acoustic mapping strategies that can be exploited in this kind of applications. A single-blade rotor was tested in a semi-anechoic chamber using a circular microphone array. <br />The Virtual Rotating Array (VRA) approach, which transforms the signals acquired by the physical static array into signals of virtual microphones synchronously rotating with the blade, hence ensuring noise-source stationarity, was used to enable the use of frequency domain acoustic mapping techniques. A comparison among three different acoustic mapping methods is presented: Conventional Beamforming, CLEAN-SC and Covariance Matrix Fitting based on Iterative Re-weighted Least Squares and Bayesian approach. The latter demonstrated to provide the best results for the application and made it possible a detailed characterization of the noise sources generated by the rotating blade at different operating conditions.</p>

Combining asynchronous microphone array measurements for enhanced acoustic imaging and volumetric source mapping

This paper explores the advantages of combining asynchronous microphone array measurements for acoustic source mapping in two and three–dimensional applications. Four different approaches are considered, three consisting of the combination of the source maps (arithmetic mean, geometric mean, and minimum value), and a fourth one obtained by combining the cross–spectral matrices of all the asynchronous measurements into a larger matrix and applying beamforming with it. Both synthetic and experimental test cases convey enhanced results concerning the single measurement baseline, especially by reducing the spurious sidelobes. Two aeroacoustic experiments are considered, the first features a distributed sound source over a flat plate model tested in a wind tunnel and the second features a hovering drone with multiple sound sources, representing typical challenges for 2D and 3D source localization, respectively. For the 2D source mapping configuration, the sidelobe level was reduced up to 5 dB with respect to the baseline, while maintaining a similar beamwidth of the main lobe. For the 3D test cases, the approaches enable volumetric source mapping capabilities, even when planar microphone arrays are considered, commonly available in typical test facilities. Overall, the minimum value approach presents the best performance for all cases in terms of reduced main lobe beamwidth and sidelobe levels.

Evaluation of the effect of microphone cavity geometries on acoustic imaging in wind tunnels

Aeroacoustic measurements performed by flush-mounted microphone arrays on the walls of closed-section wind tunnels are contaminated by the hydrodynamic pressure fluctuations of the wall’s boundary layer. This study evaluates three different microphone cavity geometries for mitigating this issue. Their improvement to the signal-to-noise ratio (SNR) and the accuracy of their acoustic imaging results are compared to a flush-mounted microphone array. The four geometries include: (1) an array of flush-mounted microphones as the baseline, (2) a cylindrical hard-plastic cavity with a countersink, (3) a conical cavity made of melamine acoustic absorbing foam, and (4) a conical cavity with star-shaped protrusions, also made of melamine. The three arrays with cavities were covered with a steel-wire cloth to reduce the boundary layer fluctuations at the microphone while the baseline array was uncovered. Two sound sources were tested in an aeroacoustic wind tunnel for assessing the performance of the different cavities: a speaker placed outside the flow and a distributed sound source generated by a flat plate inside of the flow. When using conventional frequency domain beamforming, both cavities made of melamine offer up to a 30 dB increase in SNR with respect to the flush-mounted case, followed by the hard-walled cavity with up to a 20 dB increase. This is a 20 dB improvement when compared to the single microphone cases. The melamine cavities also provide cleaner acoustic source maps and accurate spectral estimations for a wider frequency range. The effect of cavity placement and geometry on the coherence, which affects the beamforming analysis of the acoustic signal was negligible for all cases. Distributed sound source measurements using the three arrays agreed with predictions using the Brooks, Pope, and Marcolini (BPM) model, showing that the cavities could detect vortex shedding that was undetectable by the flush array.

Acoustic source imaging using densely connected convolutional networks

The localization of acoustic sources using acoustic imaging methods such as acoustic beamforming can be limited by the Rayleigh resolution limit at relatively low frequencies and the output of these methods may also produce spatial aliasing images referred to as sidelobes, particularly at high frequencies. To date, there are very few Deep Neural Network (DNN) applications for acoustic imaging to help alleviate some of these issues. In this study, several DNN models were developed with a training strategy specifically designed for an acoustic imaging task. This proposed DNN-based method is examined for various acoustic source conditions and compared with other classic acoustic imaging methods. The DNN method is able to recognize the pattern behind microphone array signals for different source positions, by using the real-component of the cross-spectral matrix of the received pressure vectors at the microphone positions. This input feature allows the DNN model to accurately locate and quantify the source strengths of complicated distributed acoustic sources, even at low frequencies that typically challenge acoustic beamforming deconvolution methods. The loss function of the DNN model is based on the difference between the estimated and true acoustic source maps, that is used to iteratively improve weighting functions within the DNN hidden layers. DNN models with both a fixed and random number of input sources are simulated for a range of specific frequencies. The DNN model with a random number of input sources is tested against conventional beamforming, CLEAN-SC and DAMAS, revealing a far improved source localization and source strength estimation. These DNN models represent a very promising proof-of-concept for the use of DNN models in the field of acoustic imaging.

IRLS based inverse methods tailored to volumetric acoustic source mapping

Planar microphone arrays are of common use in acoustic source identification methods, as well as the use of planar calculation grids. Indeed, the assumption is that the planar grid contains all sources of interest. However, this assumption may not be true in several applications and hence return misleading results. One tentative to overcome this issue is to consider three-dimensional surface adhering on the target. Unfortunately, also this choice may not be enough to obtain accurate results in challenging applications like aeroacoustic source mapping, since noise sources are not necessarily located on the surface of the target. This paper aims to analyze the issues and the benefits arising when the calculation grid turns into a volume. Two inverse methods based on Iterative Re-weighted Least Squares (IRLS) and Bayesian Regularization (BR) are formulated: Equivalent Source Method (ESM-IRLS) and Covariance Matrix Fitting (CMF-IRLS). Even though these methods are based on concepts already known in literature, the focus of this paper is on theoretical and algorithmic aspects that make them able to produce accurate volumetric acoustic maps. The methods proposed are applied both on a simulated and an experimental test case. The former is reported to highlight the difference between standard surface mapping and volumetric mapping. The latter reports an application on an airfoil in an open jet. A comparison with the CLEAN-SC approach is reported in both cases to show the performance of the proposed methods with respect to a well-known state of the art algorithm.

Improving the Sound Source Identification Performance of Sparsity Constrained Deconvolution Beamforming Utilizing SFISTA

In this paper, an alternative sparsity constrained deconvolution beamforming utilizing the smoothing fast iterative shrinkage-thresholding algorithm (SFISTA) is proposed for sound source identification. Theoretical background and solving procedures are introduced. The influence of SFISTA regularization and smoothing parameters on the sound source identification performance is analyzed, and the recommended values of the parameters are obtained for the presented cases. Compared with the sparsity constrained deconvolution approach for the mapping of acoustic sources (SC-DAMAS) and the fast iterative shrinkage-thresholding algorithm (FISTA), the proposed SFISTA with appropriate regularization and smoothing parameters has faster convergence speed, higher quantification accuracy and computational efficiency, and more insensitivity to measurement noise.

Improving the resolution of underwater acoustic image measurement by deconvolution

The conventional beamforming (CBF) map of underwater acoustic image measurement can be expressed as a convolution of the source distribution and the response of the beamformer to a point source, defined as the beam pattern or the point spread function (PSF). By deconvolving the CBF beamformer’s map, the distribution of the actual sources with a clean beam map can be obtained to improve the resolution. In this paper, the deconvolved conventional beamforming (dCv) is applied to underwater acoustic image measurement to improve the accuracy of sound source localization. Due to the point spread function of the array in near field is dependent on the focused point, which means that the PSF is shift variant, so it is necessary to use shift-variant deconvolution method. In this paper, an extended Richardson-Lucy (Ex-RL) algorithm is applied to the two-dimensional shift-variant deconvolution acoustic image measurement problem to improve resolution. The method is compared with the original-RL, classical deconvolution approach for the mapping of acoustic sources (DAMAS) and non-negative least squares (NNLS). The simulation and the experiment results verify the effectiveness of the algorithm.

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.

Inverse methods in aeroacoustic three-dimensional volumetric noise source localization and quantification

Acoustic source mapping usually involves planar microphone arrays and calculation points located on a surface at a certain distance with respect to the array. An implicit assumption that sources are located on this surface is therefore performed. However, in some application, such as aeroacoustic source identification, this assumption may be wrong and produce misleading results. For this reason, it is interesting to extend the common acoustic mapping techniques to three-dimensional volumetric mapping. Direct beamforming techniques are not suited for volumetric imaging due to poor spatial resolution in radial direction from the array centre. Therefore, more refined algorithms, like deconvolution techniques or inverse methods, are required to obtain intelligible and accurate results.This paper describes the use of inverse methods in the context of aeroacoustic three-dimensional volumetric noise imaging. An Equivalent Source Method is formulated, based on Iteratively Reweighted Least Squares and on Bayesian Regularization. Moreover, a novel approach based on CLEAN-SC as decomposition tool of Cross-Spectral-Matrix in coherent source components is presented.The method proposed is applied on an aircraft model in wind tunnel. Performance are preliminary assessed with simulated test cases. A comparative investigation in exploiting a single planar array or multiple planar arrays observing noise sources from different directions is performed.

Deconvolution approach to the mapping of acoustic sources with matching pursuit and matrix factorization

We propose a novel procedure for solving the Deconvolution Approach to the Mapping of Acoustic Sources (DAMAS) inverse problem. The proposed procedure is a two-stage, hybrid greedy/non-greedy algorithm based on orthogonal matching pursuit (OMP, step 1) and non-negative matrix factorization (NMF, step 2). The purpose of the second step is to compensate for the suboptimal nature of matching pursuit. The method has been evaluated using Monte Carlo simulations and validated on experimental data. Both simulated and experimental results were compared to a few standard methods of deconvolution. These evaluations show that the proposed method has comparable, and in some cases, higher overall accuracy than the reference methods, particularly when the actual point spread function deviates from the modeled one. Simultaneously, the calculation time is low enough for near real-time use.

Continuous scan beamforming using a rotating microphone array for mapping of acoustic sources in a soundproof chamber

Continuous scan beamforming (CSBF) is a novel approach that can improve the dynamic range of a microphone array used for source localization. In the conventional beamforming approach in which spatially fixed sensors are used, the number of sensors employed determines the dynamic range of the array. Whereas, in the CSBF approach, by employing moving sensors in a prescribed motion, the effective number of sensors (so-called virtual sensors) used for the beamforming process can be greatly increased, and therefore, it can provide enhanced dynamic ranges close to the theoretical limit. In the CSBF process, for reconstruction of the time data acquired by moving sensors, stationary microphones are used for phase referencing. At ATA Engineering, Inc., a rotating, planar configuration array of 60 microphones was built and tested in a soundproof chamber with spatially distributed acoustic sources inside. The results showed that CSBF has much better performance than the conventional beamforming that employs fixed sensors; CSBF is able to discriminate tested sources that are almost 20 dB apart, which is not possible with the conventional approach. Due to the enhanced dynamic range, CSBF provides much cleaner mapping of the distribution of sources without physically increasing the number of sensors.Continuous scan beamforming (CSBF) is a novel approach that can improve the dynamic range of a microphone array used for source localization. In the conventional beamforming approach in which spatially fixed sensors are used, the number of sensors employed determines the dynamic range of the array. Whereas, in the CSBF approach, by employing moving sensors in a prescribed motion, the effective number of sensors (so-called virtual sensors) used for the beamforming process can be greatly increased, and therefore, it can provide enhanced dynamic ranges close to the theoretical limit. In the CSBF process, for reconstruction of the time data acquired by moving sensors, stationary microphones are used for phase referencing. At ATA Engineering, Inc., a rotating, planar configuration array of 60 microphones was built and tested in a soundproof chamber with spatially distributed acoustic sources inside. The results showed that CSBF has much better performance than the conventional beamforming that employs fixed se...

An Alternative Hybrid Time-Frequency Domain Approach Based on Fast Iterative Shrinkage-Thresholding Algorithm for Rotating Acoustic Source Identification

An alternative hybrid time-frequency domain approach based on the fast iterative shrinkage-thresholding algorithm (FISTA) is presented for rotating acoustic source identification. This approach is similar to the existing approaches based on the deconvolution approach for the mapping of acoustic sources (DAMAS) and non-negative least squares (NNLS) without the sample interpolation of sound pressure signal in the time domain. The acoustic source is first identified using the time-domain tracking Delay and Sum (DAS) method. The Frequency-domain deconvolution is then performed to attenuate the sidelobes and improve the spatial resolution. The simulation and experimental results indicate that the proposed approach is robust to the sampling rate and enjoys the good spatial convergence and resolution, effective sidelobe suppression, accurate quantification, as well as high computational efficiency. In general, at a low time-domain sampling rate, the proposed approach outperforms the DAMAS- or NNLS-based hybrid time-frequency domain approaches without the sample interpolation of sound pressure signal in the time domain.

Research of the improved mapping of acoustic correlated sources method

Outstanding from other beamforming methods, the mapping of acoustic correlated sources (MACS) method has high resolution and can greatly reduce the computation amount in localizing the correlated sound sources. This study aims to overcome the drawback of MACS as insufficient sparsity resulting in excessive side lobes and deceleration in convergence speed. Those aims are achieved by the improved MACS (IMACS), relying on adaptively update the sparse constraint condition during convex optimization process. Localization maps of a pair of correlated sound sources in anechoic environment and in chamber with reflection are acquired, and some indexes are introduced to evaluate those maps. It is shown that IMACS has the similar localization error with MACS, and IMACS also has some advantages over MACS as follows. IMACS has less calculation burden, and its less quantity of side lobes and higher discrimination coefficient present better definition of the localized sound sources than MACS. The resolution of IMACS is also discussed in detail based on experimental analysis compared with other beamforming methods.

Spherical Harmonics Decomposition in inverse acoustic methods involving spherical arrays

Inverse methods for acoustic source mapping have gathered the attention of the beamforming community during the last years. Indeed, they provide higher accuracy in both source localization and strength estimation with respect to Conventional Beamforming (CB). One of the main drawbacks of the current formulations is the need of a regularization strategy for tackling the ill-posedness of the problem. Very often, Tikhonov regularization is exploited to face this issue, but different methods for estimating the regularization factor associated to the Tikhonov formulation may lead to different regularization levels and, therefore, to different results. This paper presents a way to face this problem when dealing with spherical arrays. The new approach proposed by the authors exploits Spherical Harmonics Decomposition (SHD) of complex pressure data at microphone locations. SHD performs a spatial filtering that reduces the effect of noise and causes an intrinsic stabilization of the numerical problem associated to the inverse problem formulation. When the source-receiver propagation model is appropriate to describe the acoustic environment in which the test takes place and noise is not spoiling excessively measurement data, the SHD approach is sufficient to obtain a regularized solution. If these conditions are not satisfied, SHD can be exploited as a pre-processing step in a twofold procedure also involving classical Tikhonov regularization. In this paper the SHD approach is tested in the Generalized Inverse Beamforming (GIBF) formulation. Classical Tikhonov approach, in which the regularization factor is estimated using the Generalized Cross-Validation (GCV), L-curve functions and Bayesian regularization, is presented as a way to enhance data processed by SHD. A sensitivity analysis of the approach to measurement noise and source-receiver relative positions is presented on simulated data. Results on experimental data are presented and discussed for both a simplified test case and an application to a real car cabin.

Wideband compressive beamforming tomography for drive-by large-scale acoustic source mapping.

Noise-mapping is an effective sound visualization tool for the identification of urban noise hotspots, which is crucial to taking targeted measures to tackle environmental noise pollution. This paper develops a high-resolution wideband acoustic source mapping methodology using a portable microphone array, where the joint localization and power spectrum estimation of individual sources sparsely distributed over a large region are achieved by tomographic imaging with the multi-frequency delay-and-sum beamforming power outputs from multiple array positions. Exploiting the fact that a wideband source has a common spatial signal-support across the frequency spectrum, two-dimensional tomographic maps are produced by applying compressive sensing techniques including group least absolute shrinkage selection operator formulation and sparse Bayesian learning to promote group sparsity over multiple frequency bands. The high-resolution mapping is demonstrated with experimental data recorded with a microphone array mounted atop an electric vehicle driven along a road while playing audio clips from a loudspeaker positioned within the adjacent open field.

Comparison of beamforming algorithms based on localization of calibrating sound sources and air jet noise

The paper considers study of beamforming algorithms for localization of noise sources. The mathematical formulations are briefly described for the following algorithms: Delay-and-Sum Beamforming, Cross-spectral Beamforming, Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS). Based on the mentioned algorithms the program codes were developed. Operability of the program codes was tested on virtual localization of the point sources. All algorithms demonstrated good ability to distinguish these sources at different frequencies at their close position relative to each other. Initially, experiments were based on localization of calibrating static sound sources (beepers) using Bruel & Kjaer 54-microphone array. The measured data were processed both in the Bruel & Kjaer software and in the developed software. For static point sources, all algorithms have shown good work quality. The experiments were also carried out for the localization of noise sources in a turbulent air jet. In this case, the best results were demonstrated by Cross-Spectral Beamforming algorithm.