High-Resolution Signal and Noise Field Estimation Using theL1 (Least Absolute Values) Norm (original) (raw)
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IEEE Access, 2020
Beamforming is a powerful technique to achieve acoustic imaging in far-field. However, its spatial resolution is strongly blurred by the point spread function (PSF) of phased microphone array. Due to the limitation of array aperture and microphone density, the PSF is far from Dirac delta function, so that it is difficult to obtain a high-resolution beamforming image at low-frequencies (e.g.500-1500Hz). This paper proposes a Bayesian inference method based on Non-synchronous Array Measurements (Bi-NAM) so as to refine the PSF and break through the beamforming limitation for low-frequency source imaging. Firstly, by sequentially moving prototype array at different positions, the non-synchronous measurements can get a sizeable synthetic aperture and high density of microphones. The synthetic cross-spectrum matrix (CSM) can significantly improve the beamforming performance. To confine the approximation error of synthetic CSM and the uncertainty of forward model, as well as the noise interference, a Bayesian inference based on joint maximum a posterior (JMAP) is proposed to solve an ill-posed inverse problem. A Student-t prior is employed to enforce the sparse property of acoustic strength distribution. The background noise can be adaptively modeled by the Student-t distribution, which is related to some of the typical symmetric distributions. Then the hyper-parameters in JMAP inference are efficiently estimated by the Bayesian hierarchical framework. Through experimental data, the proposed Bi-NAM approach is confirmed to achieve high-resolution acoustic imaging at 1000Hz and 800Hz, respectively, even under the Laplace noise interference. INDEX TERMS Acoustic imaging, Bayesian inference, beamforming, cross-spectrum matrix, joint maximum a posterior, non-synchronous measurements, point spread function, student-t prior.
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Conventional beamforming, among the several techniques that can be used for noise source localization, has been widely used in complex problems, including aeroacoustics applications. The sound generated by flow turbulence can present a distributed coherent source region, which presents some challenges to the conventional beamforming localization accuracy. The Generalized Inverse Beamforming (GIB) is a recent method aiming at the identification of coherent or incoherent, distributed or compact, monopole or multipole sources. This method is based on the microphone array cross-spectral eigenstructure, resulting in a robust localization technique. In this work, the performance of the GIB method is investigated for two simple cases in comparison to conventional beamforming. The first test case, a simple monopole, illustrates the frequency range accuracy, and the second test case, two monopoles in coherent radiation, illustrates the different performance in coherent scenarios. Numerical investigation is used to define the test array aperture and distance to the target region. In order to improve the generalized inverse estimation on the coherent case, a new hybrid estimation is proposed. This consists in creating a source mapping that is comparable to the conventional mapping based on the generalized inverse mapping and the array Point Spread Function. The offsetting between the hybrid mapping and the conventional mapping indicates the quality of the generalized inverse estimation and the hybrid estimation points to the actual sources overall strength.
Noise source identification by beamforming technique
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The noise produced by complex systems such as automobiles, airplanes, machines, etc. contributes to overall noise of an environment. In the way to reduce these levels, the identification of the prominent sources is necessary. In many cases the beamforming technique is capable to carry out this task creating a visual map of the sound source. The goal of this work will be to give a basic understanding of beamforming techniques including software, hardware and to illustrate its wide applicability to common noise control and vibration problems. Resumo Os ruidos produzidos por sistemas complexos como automoveis, aeronaves, maquinas, etc. contribuem para o ruido global de um ambiente. De modo a reduzir estes niveis, a identificacao das fontes proeminentes se faz necessaria. Em muitos casos, a tecnica de beamforming e capaz de realizar esta tarefa criando um mapa visual da fonte sonora. Este trabalho objetiva fornecer o entendimento basico sobre a tecnica de beamforming, incluindo software...
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An innovative technique based on beamforming is implemented, at the aim of detecting the distances from the observer and the relative positions among the noise sources themselves in multisource noise scenarios. By means of preliminary activities to assess the optical camera focal length and stereoscopic measurements followed by image processing, the geometric information in the sourcemicrophone direction is retrieved, a parameter generally missed in classic beamforming applications. A corollary of the method consists of the possibility of obtaining also the distance among different noise sources which could be present in a multisource environment. A loss of precision is found when the effect of the high acoustic reflectivity ground interferes with the noise source.
The ill-conditioning problem in sound field reconstruction
A method for the analysis and reconstruction of a three dimensional sound field using an array of microphones and an array of loudspeakers is presented. The criterion used to process the microphone signals and obtain the loudspeakers signals is based on the minimisation of the least-square error between the reconstructed and the original sound field. This approach requires the formulation of an inverse problem that can lead to unstable solutions due to the ill-conditioning of the propagation matrix. The concepts of generalised Fourier transform and singular value decomposition are introduced and applied to the solution of the inverse problem in order to obtain stable solutions and to provide a clear understanding of the regularisation method. 1.
Comparison of microphone array denoising techniques and application to flight test measurements
25th AIAA/CEAS Aeroacoustics Conference
This paper deals with the denoising of microphone array measurements. In many situations, flush mounted microphone arrays are polluted by a turbulent boundary layer, this is typically the case considering wind tunnels or inflight tests. Acoustic imaging results are strongly affected by this noise, classical approaches to solve this issue consist in removing the diagonal terms from the measured cross spectral matrix, or to implement background noise subtraction strategies. This can be sufficient for conventional beamforming approaches, but can be a limitation when implementing more advanced identification methods. This work introduces two alternative techniques, a first one based on a statistical model whose parameters are inferred from measurements (PFA-Probabilistic Factor Analysis), and a second one based on the use of noise-free references. The former is an original contribution of the work, while the later is a well known approach yet not often used in the present context. Both methods, as well as more classical approaches, are compared in the frame of inflight array measurements for the characterization of jet noise. It is shown that the proposed advanced denoising approaches show enhanced performances as compared to classical approaches when applying either conventional beamforming or inverse source characterization.
Iterative beamforming for identification of multiple broadband sound sources
Journal of Sound and Vibration, 2016
The reconstruction of broadband sound sources is an important issue in industrial acoustics. In this paper, a model comprising multiple incoherent Gaussian random sources is considered. The aim is to estimate locations and powers of the sound sources using the pressures measured by an array of microphones. Each measured pressure is interpreted as a mixture of latent signals emitted by different sound sources. Then, an Iterative Beamforming (IB) method is developed to estimate the source parameters. This approach is based on the Expectation-Maximization (EM) algorithm, a well-known iterative procedure for solving maximum likelihood parameter estimation. More specifically, IB iteratively estimates the source contributions and performs beamforming on these estimates. In this work, experiments on real data illustrate the advantage of IB with respect to classical beamforming and Near-field Acoustical Holography (NAH). In particular, the proposed method is shown to work over a wider range of frequencies, to better estimate the source locations, and is able to quantify the powers of the sources. Furthermore, experiments illustrate that IB can not only localize the sources on a given surface, but also accurately estimate their 3D locations.