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A probabilistic approach for cross-spectral matrix denoising: Benchmarking with some recent methods.
J Acoust Soc Am. 2020 May; 147(5):3108.JA

Abstract

Array measurements can be contaminated by strong noise, especially when dealing with microphones located near or in a flow. The denoising of these measurements is crucial to allow efficient data analysis or source imaging. In this paper, a denoising approach based on a Probabilistic Factor Analysis is proposed. It relies on a decomposition of the measured cross-spectral matrix (CSM) using the inherent correlation structure of the acoustical field and of the flow-induced noise. This method is compared with three existing approaches, aiming at denoising the CSM, without any reference or background noise measurements and without any information about the sources of interest. All these methods make the assumption that the noise is statistically uncorrelated over the microphones, and only one of them significantly impairs the off-diagonal terms of the CSM. The main features of each method are first reviewed, and the performances of the methods are then evaluated by way of numerical simulations along with measurements in a closed-section wind tunnel.

Authors+Show Affiliations

University of Lyon, INSA Lyon, Laboratoire Vibrations Acoustique, F-69621 Villeurbanne, France.University of Lyon, INSA Lyon, Laboratoire Vibrations Acoustique, F-69621 Villeurbanne, France.University of Lyon, INSA Lyon, Laboratoire Vibrations Acoustique, F-69621 Villeurbanne, France.University of Lyon, École Centrale de Lyon, INSA Lyon, Université Claude Bernard Lyon I, CNRS, Laboratoire de Mécanique des Fluides et d'Acoustique, UMR 5509, F-69134 Écully, France.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32486801

Citation

Dinsenmeyer, Alice, et al. "A Probabilistic Approach for Cross-spectral Matrix Denoising: Benchmarking With some Recent Methods." The Journal of the Acoustical Society of America, vol. 147, no. 5, 2020, p. 3108.
Dinsenmeyer A, Antoni J, Leclère Q, et al. A probabilistic approach for cross-spectral matrix denoising: Benchmarking with some recent methods. J Acoust Soc Am. 2020;147(5):3108.
Dinsenmeyer, A., Antoni, J., Leclère, Q., & Pereira, A. (2020). A probabilistic approach for cross-spectral matrix denoising: Benchmarking with some recent methods. The Journal of the Acoustical Society of America, 147(5), 3108. https://doi.org/10.1121/10.0001098
Dinsenmeyer A, et al. A Probabilistic Approach for Cross-spectral Matrix Denoising: Benchmarking With some Recent Methods. J Acoust Soc Am. 2020;147(5):3108. PubMed PMID: 32486801.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A probabilistic approach for cross-spectral matrix denoising: Benchmarking with some recent methods. AU - Dinsenmeyer,Alice, AU - Antoni,Jérôme, AU - Leclère,Quentin, AU - Pereira,Antonio, PY - 2020/6/4/entrez SP - 3108 EP - 3108 JF - The Journal of the Acoustical Society of America JO - J. Acoust. Soc. Am. VL - 147 IS - 5 N2 - Array measurements can be contaminated by strong noise, especially when dealing with microphones located near or in a flow. The denoising of these measurements is crucial to allow efficient data analysis or source imaging. In this paper, a denoising approach based on a Probabilistic Factor Analysis is proposed. It relies on a decomposition of the measured cross-spectral matrix (CSM) using the inherent correlation structure of the acoustical field and of the flow-induced noise. This method is compared with three existing approaches, aiming at denoising the CSM, without any reference or background noise measurements and without any information about the sources of interest. All these methods make the assumption that the noise is statistically uncorrelated over the microphones, and only one of them significantly impairs the off-diagonal terms of the CSM. The main features of each method are first reviewed, and the performances of the methods are then evaluated by way of numerical simulations along with measurements in a closed-section wind tunnel. SN - 1520-8524 UR - https://www.unboundmedicine.com/medline/citation/32486801/A_probabilistic_approach_for_cross-spectral_matrix_denoising:_Benchmarking_with_some_recent_methods DB - PRIME DP - Unbound Medicine ER -
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