A THEORETICAL AND EXPERIMENTAL COMPARISON OF THE ITERATIVE EQUIVALENT SOURCE METHOD AND THE GENERALIZED INVERSE BEAMFORMING

Many acoustic source mapping methods exist to perform noise source localization and quantification and appear to be powerful tools for acoustic diagnosis in industrial applications. Two classes of methods have known many developments in the last few decades: deconvolution algorithms combined with beamforming (CLEAN, DAMAS, etc) and inverse methods such as the Equivalent Source Method (ESM) and the Generalized Inverse Beamforming (GIB). In this paper, a special attention will be paid to the use of inverse methods in complex acoustic environments. Recently, Suzuki has demonstrated the applicability of the GIB to the study of aerodynamic sound sources [25], highlighting comparable performances to the existing deconvolution techniques. On the other hand, an iterative version of the ESM has been proposed in the context of acoustic imaging in closed spaces, at INSA Lyon [22]. This paper provides a theoretical and experimental comparison between two inverse methods: the iterative ESM and the GIB using various benchmark problems and aeroacoustic experimental data. The experimental set-up consists of a steel rod placed in the potential core of a rectangular jet inside an open-jet anechoic wind tunnel. It will be shown that both methods are based on similar mathematical formulations although they were developed for different application fields. Reconstruction performances of the algorithms in terms of localization and quantification will be discussed as well as their computational efficiency.

[1]  Jian Li,et al.  On robust Capon beamforming and diagonal loading , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[2]  A Tikhonov,et al.  Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .

[3]  R. Dougherty Improved Generalized Inverse Beamforming for Jet Noise , 2011 .

[4]  Aiaa Paper,et al.  CLEAN based on spatial source coherence , 2007 .

[5]  Per Christian Hansen,et al.  Rank-Deficient and Discrete Ill-Posed Problems , 1996 .

[6]  Antoine Peillot,et al.  Imagerie acoustique par approximations parcimonieuses des sources , 2012 .

[7]  Jesper Gomes,et al.  A study on regularization parameter choice in Near‐field Acoustical Holography , 2008 .

[8]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[9]  Q. Leclère Acoustic imaging using under-determined inverse approaches : Frequency limitations and optimal regularization , 2009 .

[10]  Robert P. Dougherty,et al.  Advanced Time-Domain Beamforming Techniques , 2004 .

[11]  Takao Suzuki L1 generalized inverse beam-forming algorithm resolving coherent/incoherent, distributed and multipole sources , 2011 .

[12]  J. Antoni A Bayesian approach to sound source reconstruction: optimal basis, regularization, and focusing. , 2012, The Journal of the Acoustical Society of America.

[13]  Dianne P. O'Leary,et al.  The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems , 1993, SIAM J. Sci. Comput..

[14]  Rémi Gribonval,et al.  Near-field acoustic holography using sparse regularization and compressive sampling principles. , 2012, The Journal of the Acoustical Society of America.

[15]  Takao Suzuki Generalized Inverse Beam-forming Algorithm Resolving Coherent/Incoherent, Distributed and Multipole Sources , 2008 .

[16]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[17]  Wim Desmet,et al.  GENERALIZED INVERSE BEAMFORMING INVESTIGATION AND HYBRID ESTIMATION , 2010 .

[18]  Henry Cox,et al.  Robust adaptive beamforming , 2005, IEEE Trans. Acoust. Speech Signal Process..

[19]  P. Hansen,et al.  Exploiting Residual Information in the Parameter Choice for Discrete Ill-Posed Problems , 2006 .

[20]  Philip A. Nelson,et al.  Optimal regularisation for acoustic source reconstruction by inverse methods , 2004 .

[21]  J. D. Maynard,et al.  Nearfield acoustic holography: I. Theory of generalized holography and the development of NAH , 1985 .

[22]  Ali Mohammad-Djafari,et al.  Robust Bayesian super-resolution approach via sparsity enforcing a priori for near-field aeroacoustic source imaging , 2013 .

[23]  D. Thompson,et al.  Comparison of methods for parameter selection in Tikhonov regularization with application to inverse force determination , 2007 .

[24]  J. B. Fahnline,et al.  A method for computing acoustic fields based on the principle of wave superposition , 1989 .

[25]  Antonio Pereira,et al.  Acoustic imaging in enclosed spaces , 2013 .

[26]  Thomas F. Brooks,et al.  A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) Determined from Phased Microphone Arrays , 2004 .