Level Estimation of Extended Acoustic Sources Using a Parametric Method

Blacodon, Daniel; Elias, G.

doi:10.2514/1.3053

Cited by 50 publications

(41 citation statements)

References 20 publications

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“…obtained by solving equation (3) will allow characterizing the actual levels of the acoustic sources as it was shown with numerical simulations presented in [4].…”

Section: Least Squares Solution For Source Levels Estimation With Noimentioning

confidence: 87%

“…It is characterized by a coherent noise 3 S of PSD 3 Ω at the same location of one of 2 S , and an incoherent broadband noise 4 S of variance 2 B σ , as depicted in Fig. 1.…”

Section: Problem Of the Source Levels Estimation Strating From Noisy mentioning

confidence: 99%

“…A photograph and a schematic view of the experimental set-up are presented in Fig.5 and Fig.6 respectively. We consider here that the acoustic sources are three driver units 1 S , 2 S and 3 S mounted in the middle of a wooden plate and spatially separated at 0.29 m. The background noise is generated by the driver units 4 S and 5 S placed near the array of microphones and between the two wooden plates separated by 3 meters. The phased array is composed of 30 microphones, ½ in.…”

Section: Experimental Simulationsmentioning

confidence: 99%

“…Array processing is currently used in acoustic tests run in wind tunnels to localize and rank the acoustic sources based on a model of uncorrelated monopole sources [1][2][3][4][5][6]. It has been successfully applied in the characterization of airframe noise of an aircraft model [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The present study is focused in the presentation on an extension of the Spectral Estimation Method (SEM) [4], which is based on a prior knowledge of a noise reference. This new method called Spectral Estimation Method With Additive Noise (SEMWAN) has the advantage of being able to reduce the smearing effect due to the array response and at the same time the inaccuracy of the results caused by noise sources which can be coherent as well as incoherent, with high or low levels.…”

Section: Introductionmentioning

confidence: 99%

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Spectral estimation method for noisy data using a noise reference

Blacodon

2011

Applied Acoustics

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The development of array processing methods to extract the useful characteristics of acoustic sources such as their locations and absolute levels, starting from the measured sound field is one of the main issues in aero-acoustics. Generally, the methods are based on a deconvolution operation to remove the undesirable effects of smearing produced by array response. This process should be carried out after the additive noise has been suitably attenuated and, ideally, the deconvolution operator should amplify the noise as little as possible. We show that when a reference of noise is known beforehand, and under certain assumptions, that it is possible both to remove the smearing effect produced by array response and to reduce the noise contamination of the results using a method called Spectral Estimation Method With Additive Noise. This method has been applied to computer and experimental simulations involving acoustic sources radiating in a noisy environment. The levels of the sources were found with a good accuracy and the background noise highly reduced, confirming the validity of the approach and the good performance of the proposed method.

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“…obtained by solving equation (3) will allow characterizing the actual levels of the acoustic sources as it was shown with numerical simulations presented in [4].…”

Section: Least Squares Solution For Source Levels Estimation With Noimentioning

confidence: 87%

“…It is characterized by a coherent noise 3 S of PSD 3 Ω at the same location of one of 2 S , and an incoherent broadband noise 4 S of variance 2 B σ , as depicted in Fig. 1.…”

Section: Problem Of the Source Levels Estimation Strating From Noisy mentioning

confidence: 99%

Section: Experimental Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral estimation method for noisy data using a noise reference

Blacodon

2011

Applied Acoustics

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Akustische Antennen

Michel

Möser

2009

Messtechnik Der Akustik

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Sparse $$\ell _{1}$$ ℓ 1 regularisation of matrix valued models for acoustic source characterisation

et al. 2017

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We present a strategy for the recovery of a sparse solution of a common problem in acoustic engineering, which is the reconstruction of sound source levels and locations applying microphone array measurements. The considered task bears similarities to the basis pursuit formalism but also relies on additional model assumptions that are challenging from a mathematical point of view. Our approach reformulates the original task as a convex optimisation model. The sought solution shall be a matrix with a certain desired structure. We enforce this structure through additional constraints. By combining popular splitting algorithms and matrix differential theory in a novel framework we obtain a numerically efficient strategy. Besides a thorough theoretical consideration we also provide an experimental setup that certifies the usability of our strategy. Finally, we also address practical issues, such as the handling of inaccuracies in the measurement and corruption of the given data. We provide a post processing step that is capable of yielding an almost perfect solution in such circumstances.

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Level Estimation of Extended Acoustic Sources Using a Parametric Method

Cited by 50 publications

References 20 publications

Spectral estimation method for noisy data using a noise reference

Spectral estimation method for noisy data using a noise reference

Akustische Antennen

Sparse $$\ell _{1}$$ ℓ 1 regularisation of matrix valued models for acoustic source characterisation

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