Uncorrelated Noise Sources Separation Using Inverse Beamforming

  • Claudio ColangeliEmail author
  • Paolo Chiariotti
  • Karl Janssens
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


The separation of a measured sound field in uncorrelated sources distributions can be very useful when dealing with sound source localization problems. The use of the Principal Component Analysis (PCA) principle, combined with a Generalized Inverse Beamforming (GIBF) technique, offers the possibility to resolve complex and partially correlated sound sources distributions.

Despite very promising, this approach appears still to be optimized and the influence of a number of potentially influent parameters is to be understood. In this paper a developed GIBF algorithm is combined with a PCA and firstly tested on a simulated problem, then applied on gradually more complex real cases. A sensitivity analysis on some relevant parameters is carried out in order to evaluate the robustness of the developed algorithm and the effectiveness of the used PCA.


Beamforming Inverse beamforming Sound source identification Principal component analysis Array methods 

List of Acronyms


Principal Component Analysis


Generalized Inverse Beamforming


Singular Values Decomposition


Modal Assurance Criterion


Cross-Spectral Matrix


Auto Power Spectrum


Generalized Cross-Validation function



The present research work is conducted in the frame of the Marie Curie ITN project: “ENHANCED” – GA FP7-606800. The whole consortium is gratefully acknowledged.


  1. 1.
    Suzuki T (2008) Generalized inverse beamforming algorithm resolving coherent/incoherent, distributed and multipole sources. In: 14th AIAA/CEAS aeroacoustics conference (29th AIAA aeroacoustics conference), Vancouver, 5–7 May 2008Google Scholar
  2. 2.
    Muller TJ (2002) Aeroacoustic measurements. Springer, BerlinGoogle Scholar
  3. 3.
    Zavala P (2012) Aeroacoustic source and moving source identification. PhD thesis, Faculty of Mech. Eng., University of Campinas, Sao PauloGoogle Scholar
  4. 4.
    Dougherty RP (2011) Improved generalized inverse beamforming for jet noise. In: 17th AIAA/CEAS aeroacoustics conference, AIAA 2011–2769Google Scholar
  5. 5.
    Zavala P, De Roeck W, Janssens K, Arruda JRF, Sas P, Desmet W (2010) Monopole and dipole identification using generalized inverse beamforming. In: 16th AIAA/CEAS aeroacoustics conference, Stockholm, SwedenGoogle Scholar
  6. 6.
    Zavala P, De Roeck W, Janssens K, Arruda JRF, Sas P, Desmet W (2011) Generalized inverse beamforming with optimized regularization strategy. Mech Syst Signal Process 25:928–939CrossRefGoogle Scholar
  7. 7.
    Hansen PC (1994) Regularization tools: a matlab package for analysis and solution of discrete ill-posed problems. Num Algorithms 6:1–35CrossRefzbMATHGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2015

Authors and Affiliations

  • Claudio Colangeli
    • 1
    Email author
  • Paolo Chiariotti
    • 2
  • Karl Janssens
    • 1
  1. 1.Siemens Industry Software NVLeuvenBelgium
  2. 2.Università Politecnica delle MarcheAnconaItaly

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