Acoustic beamforming for noise source localization – Reviews, methodology and applications

Chiariotti, Paolo; Martarelli, Milena; Castellini, Paolo

doi:10.1016/j.ymssp.2018.09.019

Cited by 279 publications

(112 citation statements)

References 96 publications

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“…Over the following years, different algorithms and approaches have been developed, whereby the level of complexity has also increased, benefitting from the improvement in data acquisition and computer computation performances. Most of these algorithms will be presented in the following sections of this paper; nevertheless, it might be here beneficial to provide some basic concepts of beamforming by presenting the so-called Delay-and-Sum (D&S) approach in the time domain [2,6]. CRITICAL PART The sound generated by aircraft propagates to the listener using a multitude of paths (e.g.…”

Section: Theory and Methodsmentioning

confidence: 99%

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Acoustic Camera Scanning as a Detection of Noise Sources on Small Aircraft

Juřička

Juřičková

2020

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This article is focused on the dynamic detection of sound/noise sources outside aircraft that can be detected as exposition. Over the last few years methods were developed for detecting noise sources in area of algorithm uses in detection methods, new procedures and devices. Nowadays, there are very important methods for quick and easy detection of a sound defect on the surface of an aircraft fuselage, components and accessories. All parts that are on the fuselage generated specific noise to the free field.

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Section: Theory and Methodsmentioning

confidence: 99%

“…The aim of test procedures was to find the noise source for the propeller or exhaust. The procedure using the acoustic camera experiment revealed significant sound annoyance reductions generated by sources in lowfrequency ranges in small aircraft [6].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Camera Scanning as a Detection of Noise Sources on Small Aircraft

Juřička

Juřičková

2020

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“…tion [1]- [4]. However, the spatial resolution of the CBF map is strongly blurred by the spatial impulse response or point spread function (PSF) of phased microphone array [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A High-Resolution and Low-Frequency Acoustic Beamforming Based on Bayesian Inference and Non-Synchronous Measurements

Chu

Ning

et al. 2020

IEEE Access

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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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“…The acoustic imaging techniques have played a central role in the Noise, Vibration, and Harshness (NVH) engineering field of sound source identification, such as automotive, aircraft, and industrial applications [ 1 , 2 , 3 ]. As one of the excellent acoustic imaging tools, near-field acoustical holography (NAH) was first formalized four decades ago by the groundbreaking work of Williams [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Ratio Model of L1/L2 Norm for Sound Source Identification

Huang

Zhang

et al. 2020

Sensors

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In the field of sound source identification, robust and accurate identification of the targeted source could be a challenging task. Most of the existing methods select the regularization parameters whose value could directly affect the accuracy of sound source identification during the solving processing. In this paper, we introduced the ratio model ℓ1/ℓ2 norm to identify the sound source(s) in the engineering field. Using the alternating direction method of multipliers solver, the proposed approach could avoid the selection of the regularization parameter and localize sound source(s) with robustness at low and medium frequencies. Compared with other three methods employing classical penalty functions, including the Tikhonov regularization method, the iterative zoom-out-thresholding algorithm and the fast iterative shrinkage-thresholding algorithm, the Monte Carlo Analysis shows that the proposed approach with ℓ1/ℓ2 model leads to stable sound pressure reconstruction results at low and medium frequencies. The proposed method demonstrates beneficial distance-adaptability and signal-to-noise ratio (SNR)-adaptability for sound source identification inverse problems.

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Acoustic beamforming for noise source localization – Reviews, methodology and applications

Cited by 279 publications

References 96 publications

Acoustic Camera Scanning as a Detection of Noise Sources on Small Aircraft

Acoustic Camera Scanning as a Detection of Noise Sources on Small Aircraft

A High-Resolution and Low-Frequency Acoustic Beamforming Based on Bayesian Inference and Non-Synchronous Measurements

A Ratio Model of L1/L2 Norm for Sound Source Identification

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