Functional Generalized Inverse Beamforming Based on the Double-Layer Microphone Array Applied to Separate the Sound Sources

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Shu Li ◽  
Zhongming Xu ◽  
Yansong He ◽  
Zhifei Zhang ◽  
Shaoyu Song
Keyword(s):  
Double Layer ◽  
Dynamic Range ◽  
Generalized Inverse ◽  
Microphone Array ◽  
A Priori ◽  
Sound Sources ◽  
Popular Method ◽  
Array Measurements ◽  
Two Sides ◽  
Regularization Matrix

Beamforming based on microphone array measurements is a popular method for identifying sound sources. However, beamforming has many limitations that limit their precision. These limitations are addressed in research. To separate the contributions which come from two sides of the microphone array more accurately, an innovative beamforming method based on a double-layer microphone array, called functional generalized inverse beamforming (FGIB), is proposed to improve beamforming performance. This method, which involves the use of a priori beamforming regularization matrix and a matrix function to redefine the inverse problem, is combined with the advantages of both generalized inverse beamforming (GIB) and functional beamforming. Compared with GIB, with reduced iterations, the computational efficiency of FGIB is greatly improved. The dynamic range of the proposed method can be modestly improved as order v increases. Furthermore, the sidelobes gradually disappear and the mainlobes become narrower. Both simulations and experiments have shown that the sources are correctly located and separated. The proposed FGIB demonstrates the good performance when compared to other beamforming methods in terms of resolution and sidelobes level.

Functional generalized inverse beamforming with regularization matrix applied to sound source localization

2016 ◽  
Vol 23 (18) ◽  
pp. 2977-2988 ◽  
Author(s):  
Shu Li ◽  
Zhongming Xu ◽  
Zhifei Zhang ◽  
Yansong He ◽  
Jin Mao
Keyword(s):  
Source Localization ◽  
Dynamic Range ◽  
Generalized Inverse ◽  
Free Field ◽  
Computation Time ◽  
Side Lobe ◽  
Sound Sources ◽  
Low Frequencies ◽  
Common Application ◽  
Regularization Matrix

Microphone arrays have become a popular technique to identify sound sources. They can be utilized to localize the sources for various applications. The most common application is the conventional beamforming that provides the source maps with strong side lobes and poor spatial resolution at low frequencies. To overcome these problems, the focus is set on deconvolution and generalized inverse techniques such as a deconvolution approach for the mapping of acoustic sources (DAMAS) and generalized inverse beamforming (GIB). Although the source maps are clearly improved, these methods have the shortcomings of expensive computing and limited dynamic range. In this paper, we propose a source localization method called functional generalized inverse beamforming with regularization matrix (FGIBR) based on an inverse problem. Compared with GIB, the accuracy of FGIBR could be improved by introducing a new beamforming regularization matrix and a scaling parameter c0. Also the dynamic range of the source maps can be increased by applying FGIBR with an exponent parameter called order v. Several simulated examples are given to illustrate that the side lobes are suppressed and the main lobe becomes much narrow; moreover, if order v is increased, the beamforming side lobes can be sharply reduced and the actual position of the noise source can be precisely located. Then FGIBR is implemented to deal with experimental data in the free field. In the case of the experiment, the source is correctly located. The proposed FGIBR demonstrates a good performance in terms of resolution and side lobe rejection compared with other beamforming methods. Furthermore, the computation time is shown to be low if the iteration and order are reasonable.

scholarly journals Acoustic source localization using the open spherical microphone array in the low-frequency range

2019 ◽  
Vol 283 ◽  
pp. 04001
Author(s):  
Boquan Yang ◽  
Shengguo Shi ◽  
Desen Yang
Keyword(s):  
Source Localization ◽  
Generalized Inverse ◽  
Microphone Array ◽  
Low Frequency ◽  
Poor Performance ◽  
Microphone Arrays ◽  
Three Dimension ◽  
Frequency Range ◽  
Array Aperture ◽  
Regularization Matrix

Recently, spherical microphone arrays (SMA) have become increasingly significant for source localization and identification in three dimension due to its spherical symmetry. However, conventional Spherical Harmonic Beamforming (SHB) based on SMA has limitations, such as poor resolution and high side-lobe levels in image maps. To overcome these limitations, this paper employs the iterative generalized inverse beamforming algorithm with a virtual extrapolated open spherical microphone array. The sidelobes can be suppressed and the main-lobe can be narrowed by introducing the two iteration processes into the generalized inverse beamforming (GIB) algorithm. The instability caused by uncertainties in actual measurements, such as measurement noise and configuration problems in the process of GIB, can be minimized by iteratively redefining the form of regularization matrix and the corresponding GIB localization results. In addition, the poor performance of microphone arrays in the low-frequency range due to the array aperture can be improved by using a virtual extrapolated open spherical array (EA), which has a larger array aperture. The virtual array is obtained by a kind of data preprocessing method through the regularization matrix algorithm. Both results from simulations and experiments show the feasibility and accuracy of the method.

Localisation of Sound Sources on Aircraft in Flight

2012 ◽  
Author(s):  
Henri A. Siller
Keyword(s):  
Dynamic Range ◽  
Microphone Array ◽  
Acoustic Power ◽  
Microphone Arrays ◽  
Sound Sources ◽  
Noise Abatement ◽  
Source Regions ◽  
The Time Domain ◽  
Imaging Properties ◽  
Source Signals

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 unified formalism for acoustic imaging based on microphone array measurements

2017 ◽  
Vol 16 (4-5) ◽  
pp. 431-456 ◽  
Author(s):  
Q Leclère ◽  
A Pereira ◽  
C Bailly ◽  
J Antoni ◽  
C Picard
Keyword(s):  
Microphone Array ◽  
Imaging Techniques ◽  
A Priori ◽  
Supersonic Jet ◽  
Point Of View ◽  
Problem Definition ◽  
Theoretical Point ◽  
Array Measurements ◽  
Equivalent Sources ◽  
Application Fields

The problem of localizing and quantifying acoustic sources from a set of acoustic measurements has been addressed, in the last decades, by a huge number of scientists, from different communities (signal processing, mechanics, physics) and in various application fields (underwater, aero, or vibro acoustics). This led to the production of a substantial amount of literature on the subject, together with the development of many methods, specifically adapted and optimized for each configuration and application field, the variety and sophistication of proposed algorithms being sustained by the constant increase in computational and measurement capabilities. The counterpart of this prolific research is that it is quite tricky to get a clear global scheme of the state of the art. The aim of the present work is to make an attempt in this direction, by proposing a unified formalism for different well known imaging techniques, from identification methods (acoustic holography, equivalent sources, Bayesian focusing, Generalized inverse beamforming…) to beamforming deconvolution approaches (DAMAS, CLEAN). The hypothesis, advantages and pitfalls of each approach will be established from a theoretical point of view, with a particular effort in trying to separate differences in the problem definition (a priori information, main assumptions) and in the algorithms used to find the solution. Numerical simulations will be proposed for different source configurations (coherent/incoherent/extended/sparse distributions), and an experimental illustration on a supersonic jet will be finally discussed.

scholarly journals DESIGNING PLANAR MICROPHONE ARRAY FOR SOUND SOURCE LOCALIZATION

Akustika ◽  
2019 ◽  
Vol 32 ◽  
pp. 123-129 ◽  
Author(s):  
Victor Ershov ◽  
Vadim Palchikovskiy
Keyword(s):  
Inclination Angle ◽  
Source Localization ◽  
Sound Source ◽  
Dynamic Range ◽  
Microphone Array ◽  
Sound Source Localization ◽  
Maximum Diameter ◽  
Design Parameters ◽  
Sound Sources ◽  
The Individual

Mathematical background for designing planar microphone array for localization of sound sources are described shortly. The designing is based on optimization of objective function, which is maximum dynamic range of sound source localization. The design parameters are radial coordinates (distance along the beam from the center of the array) and angle coordinates (beam inclination) of the microphones. It is considered the arrays with the same radial coordinates of the microphones for each beam and the independent radial coordinates of each microphone, as well as the same inclination angle for all beams and the individual inclination angle of each beam. As constraints, it is used the minimum allowable distance between two adjacent microphones, and minimum and maximum diameter of the working area of the array. The solution of the optimization problem is performed by the Minimax method. An estimation of the resolution quality of designed arrays was carried out based on localization of three monopole sources. The array of 3 m in diameter without inclination of the beams and with different radial coordinates of the microphones on each beam was found to be the most efficient configuration among the considered ones.

scholarly journals Sound source localization – state of the art and new inverse scheme

2021 ◽  
Author(s):  
Stefan Gombots ◽  
Jonathan Nowak ◽  
Manfred Kaltenbacher
Keyword(s):  
Boundary Conditions ◽  
Source Localization ◽  
Microphone Array ◽  
Sound Field ◽  
Source Model ◽  
Source Distribution ◽  
Axial Fan ◽  
Sound Sources ◽  
Array Measurements ◽  
Common Technique

AbstractAcoustic source localization techniques in combination with microphone array measurements have become an important tool for noise reduction tasks. A common technique for this purpose is acoustic beamforming, which can be used to determine the source locations and source distribution. Advantages are that common algorithms such as conventional beamforming, functional beamforming or deconvolution techniques (e.g., Clean-SC) are robust and fast. In most cases, however, a simple source model is applied and the Green’s function for free radiation is used as transfer function between source and microphone. Additionally, without any further signal processing, only stationary sound sources are covered. To overcome the limitation of stationary sound sources, two approaches of beamforming for rotating sound sources are presented, e.g., in an axial fan.Regarding the restrictions concerning source model and boundary conditions, an inverse method is proposed in which the wave equation in the frequency domain (Helmholtz equation) is solved with the corresponding boundary conditions using the finite element method. The inverse scheme is based on minimizing a Tikhonov functional matching measured microphone signals with simulated ones. This method identifies the amplitude and phase information of the acoustic sources so that the prevailing sound field can be with a high degree of accuracy.

scholarly journals Complex wavefront sensing based on coherent diffraction imaging using vortex modulation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rujia Li ◽  
Liangcai Cao
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
A Priori ◽  
Measured Intensity ◽  
Physical Constraints ◽  
Coherent Diffraction Imaging ◽  
Effective Dynamic ◽  
Ill Posed ◽  
Retrieval Problem ◽  
Diffraction Imaging

AbstractPhase retrieval seeks to reconstruct the phase from the measured intensity, which is an ill-posed problem. A phase retrieval problem can be solved with physical constraints by modulating the investigated complex wavefront. Orbital angular momentum has been recently employed as a type of reliable modulation. The topological charge l is robust during propagation when there is atmospheric turbulence. In this work, topological modulation is used to solve the phase retrieval problem. Topological modulation offers an effective dynamic range of intensity constraints for reconstruction. The maximum intensity value of the spectrum is reduced by a factor of 173 under topological modulation when l is 50. The phase is iteratively reconstructed without a priori knowledge. The stagnation problem during the iteration can be avoided using multiple topological modulations.

scholarly journals Aeroacoustic noise reduction by application of end plates on wall-mounted finite airfoils

2021 ◽  
Vol 62 (5) ◽  
Author(s):  
Erik Schneehagen ◽  
Thomas F. Geyer ◽  
Ennes Sarradj ◽  
Danielle J. Moreau
Keyword(s):  
Noise Reduction ◽  
Microphone Array ◽  
Boundary Layer Transition ◽  
Tip Vortex ◽  
Layer Transition ◽  
Aeroacoustic Noise ◽  
End Plate ◽  
Array Measurements ◽  
Field Noise ◽  
Naca 0012

Abstract One known method to reduce vortex shedding from the tip of a blade is the use of end plates or winglets. Although the aerodynamic impact of such end plates has been investigated in the past, no studies exist on the effect of such end plates on the far-field noise. The aeroacoustic noise reduction of three different end-plate geometries is experimentally investigated. The end plates are applied to the free end of a wall-mounted symmetric NACA 0012 airfoil and a cambered NACA 4412 airfoil with an aspect ratio of 2 and natural boundary layer transition. Microphone array measurements are taken in the aeroacoustic open-jet wind tunnel at BTU Cottbus-Senftenberg for chord-based Reynolds numbers between 75,000 and 225,000 and angles of attack from 0$$^\circ$$ ∘ to 30$$^\circ$$ ∘ . The obtained acoustic spectra show a broad frequency hump for the airfoil base configurations at higher angles of attack that is attributed to tip noise. Hot-wire measurements taken for one configuration show that the application of an end plate diffuses the vorticity at the tip. The aeroacoustic noise contribution of the tip can be reduced when the endplates are applied. This reduction is most effective for higher angles of attack, when the tip vortex is the dominant sound source. Graphic abstract

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

2021 ◽  
Vol 182 ◽  
pp. 108247
Author(s):  
Lourenço Tércio Lima Pereira ◽  
Roberto Merino-Martínez ◽  
Daniele Ragni ◽  
David Gómez-Ariza ◽  
Mirjam Snellen
Keyword(s):  
Microphone Array ◽  
Acoustic Imaging ◽  
Array Measurements

Locating and tracking sound sources on a horizontal axis wind turbine using a compact microphone array based on beamforming

2019 ◽  
Vol 146 ◽  
pp. 295-309 ◽  
Author(s):  
Cui Qing Zhang ◽  
Zhi Ying Gao ◽  
Yong Yan Chen ◽  
Yuan Jun Dai ◽  
Jian Wen Wang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Microphone Array ◽  
Horizontal Axis ◽  
Horizontal Axis Wind Turbine ◽  
Sound Sources
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