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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)

Abstract

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.

Keywords

Beamforming Inverse beamforming Sound source identification Principal component analysis Array methods 

List of Acronyms

PCA

Principal Component Analysis

GIBF

Generalized Inverse Beamforming

SVD

Singular Values Decomposition

MAC

Modal Assurance Criterion

CSM

Cross-Spectral Matrix

APS

Auto Power Spectrum

GCV

Generalized Cross-Validation function

Notes

Acknowledgments

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.

References

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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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