F. Fahy and J. Walker, Advanced Applications in Acoustics, Noise and Vibration, 2004.

Q. Leclère, Étude et développement de la mesure indirecte d'efforts: Application à l'identification des sources internes d'un moteur Diesel (Research and development of the indirect measure of efforts: application on the identification of internal sources of a Diesel engine) Doctoral dissertation from GMC, p.165, 2003.

C. Pezerat, Q. Leclère, and N. Totaro, Identification of vibration excitations from acoustic measurements using near field acoustic holography and the force analysis technique. Sound and Vibration, pp.540-556, 2009.

Q. Leclère and C. Pezerat, Vibration source identification using corrected finite difference schemes. Sound and Vibration, pp.1366-1377, 2012.

P. Sijtsma, Using phased array beamforming to identify broadband noise sources in a turbofan engine. Key Note Presentaions of 12th CEAS-ASC Workshop Turbomachinery Broadband Noise, pp.23-24, 2008.

B. Lafon, Séparation de sources couplée aux techniques d'imagerie acoustique: Application au rayonnement de moteurs Diesel (Separation of sources coupled with techniques of acoustic imaging: application on the influence of Diesel engines) Doctoral dissertation

B. Lafon, J. Antoni, and M. , Sidahmed et al. The concept of cyclic sound intensity and its application to acoustical imaging. Sound and Vibration, pp.2107-2121, 2011.

J. Billingsley and R. Kinns, The acoustic telescope. Sound and Vibration, pp.485-510, 1976.

U. Michel, History of acoustic beamforming. The 1st Berlin Beamforming Conference (BeBeC), pp.1-17, 2006.

J. D. Maynard, E. G. Williams, and Y. Lee, Nearfield acoustic holography:?. Theory of generalized holography and the development of NAH, pp.1395-1413, 1985.

J. Hald, Basic theory and properties of statistically optimized near-field acoustical holography, The Journal of the Acoustical Society of America, vol.125, issue.4, pp.2105-2120, 2009.
DOI : 10.1121/1.3079773

F. Jacobsen and V. Jaud, Statistically optimized near field acoustic holography using an array of pressure-velocity probes, The Journal of the Acoustical Society of America, vol.121, issue.3, pp.1550-1558, 2007.
DOI : 10.1121/1.2434245

S. F. Wu, On reconstruction of acoustic pressure fields using the Helmholtz equation least squares method, The Journal of the Acoustical Society of America, vol.107, issue.5, pp.2511-2522, 2000.
DOI : 10.1121/1.428639

A. Sarkissian, Extension of measurement surface in near-field acoustic holography, The Journal of the Acoustical Society of America, vol.115, issue.4, pp.1593-1596, 2004.
DOI : 10.1121/1.1645609

Q. Leclère, Acoustic imaging using under-determined inverse approaches: Frequency limitations and optimal regularization, Journal of Sound and Vibration, vol.321, issue.3-5, pp.605-619, 2009.
DOI : 10.1016/j.jsv.2008.10.022

A. Pereira, Acoustic imaging in enclosed spaces Doctoral dissertation from GMC, 2013.

A. Schuhmacher, J. Hald, and K. B. Rasmussen, Sound source reconstruction using inverse boundary element calculations, The Journal of the Acoustical Society of America, vol.113, issue.1, pp.114-127, 2003.
DOI : 10.1121/1.1529668
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.2167

A. Oey, H. W. Jang, and J. G. Ih, Effect of sensor proximity over the non-conformal hologram plane in the near-field acoustical holography based on the inverse boundary element method. Sound and Vibration, pp.2083-2098, 2010.

M. B. Magalhães and R. A. Tenenbaum, Sound sources reconstruction techniques: A review of their evolution and new trends, Acta Acustica united with Acustica, vol.90, pp.199-220, 2004.

S. F. Wu, Methods for reconstructing acoustic quantities based on acoustic pressure measurements, The Journal of the Acoustical Society of America, vol.124, issue.5, pp.2680-2697, 2008.
DOI : 10.1121/1.2977731

E. J. Candes, J. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006.
DOI : 10.1109/TIT.2005.862083
URL : http://arxiv.org/abs/math/0409186

E. J. Candes, J. Romberg, and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics, vol.7, issue.8, pp.1207-1223, 2006.
DOI : 10.1002/cpa.20124

D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486

G. Chardon, L. Daudet, and A. Peillot, Near-field acoustic holography using sparse regularization and compressive sampling principles, The Journal of the Acoustical Society of America, vol.132, issue.3, pp.1521-1534, 2012.
DOI : 10.1121/1.4740476

J. Antoni, A Bayesian approach to sound source reconstruction: Optimal basis, regularization, and focusing, The Journal of the Acoustical Society of America, vol.131, issue.4, pp.2873-2890, 2012.
DOI : 10.1121/1.3685484
URL : https://hal.archives-ouvertes.fr/hal-00776324

Y. H. Kim, Can we hear the shape of a noise source? The 18th International Congress on Acoustics, pp.3357-3370, 2004.

E. F. Grande, Near-field acoustic holography with sound pressure and particle velocity measurements, 0200.

N. Chu, Bayesian approach in acoustic source localization and imaging. Doctoral dissertation from L2S, Paris : University of Paris, vol.11, issue.227, 2013.
URL : https://hal.archives-ouvertes.fr/tel-01016528

P. Comon, Independent component analysis: A new concept? Signal Processing

J. F. Cardoso, Blind signal Separation: Statistical principles. Proceeding of the IEEE, pp.2009-2025, 1998.
DOI : 10.1109/5.720250
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.7237

A. J. Bell and T. J. Sejnowski, An information-maximisation approach to blind separation and blind deconvolution, Neural Computation, vol.7, pp.1004-1034, 1995.

T. W. Lee, M. Girolami, and A. J. Bell, A unifying information-theoretic framework for independent component analysis, Computers & Mathematics with Applications, vol.39, issue.11, pp.1-21, 2000.
DOI : 10.1016/S0898-1221(00)00101-2

X. R. Cao and R. W. Liu, General approach to blind source separation, IEEE Transactions on Signal Processing, vol.44, pp.562-571, 1996.

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

K. E. Hild, I. , D. Erdogmus, and J. C. Principe, An analysis of entropy estimators for blind source separation, Signal Processing, vol.86, issue.1, pp.181-194, 2006.
DOI : 10.1016/j.sigpro.2005.04.015

L. F. Guo and M. Garland, The use of entropy minimization for the solution of blind source separation problems in image analysis, Pattern Recognition, vol.39, issue.6, pp.1066-1073, 2006.
DOI : 10.1016/j.patcog.2005.09.006

S. Shwartz, Y. Y. Schechner, and M. Zibulevsky, Blind separation of convolutive image mixtures, Neurocomputing, vol.71, issue.10-12, pp.2164-2179, 2008.
DOI : 10.1016/j.neucom.2007.07.042

X. L. Li and T. Adali, Blind Separation of Noncircular Correlated Sources Using Gaussian Entropy Rate, IEEE Transactions on Signal Processing, vol.59, issue.6, pp.2969-2975, 2011.
DOI : 10.1109/TSP.2011.2114653

T. Pham, Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion, Signal Processing, vol.81, issue.4, pp.855-870, 2001.
DOI : 10.1016/S0165-1684(00)00260-7

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, pp.147-163, 2001.

A. Cichocki and S. Amari, Adaptive blind signal and image processing ? Learning algorithms and applications, 2002.

P. Comon and C. Jutten, Handbook of Blind Source Separation: Independent Component Analysis and Application, p.p, 2010.

J. Hérault and C. Jutten, Space or time adaptive signal processing by neural network models, AIP Conference Proceedings, pp.206-211, 1986.
DOI : 10.1063/1.36258

S. Amari and A. Cichocki, Adaptive blind signal processing-neural network approaches, Proceedings of the IEEE, pp.2026-2048, 1998.
DOI : 10.1109/5.720251
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.1631

J. F. Cardoso and A. Souloumiac, Blind beamforming for non Gaussian signals. Radar and Signal Processing, IEEE Proceedings F, vol.140, pp.362-370, 1993.

A. Belouchrani, K. A. Meraim, and J. F. Cardoso, A blind source separation technique using second-order statistics, IEEE Transactions on Signal Processing, vol.45, issue.2, pp.434-444, 1997.
DOI : 10.1109/78.554307

R. Gribonval and S. Lesage, A survey of sparse components analysis for blind source separation: Principle, perspectives and new challenges, European Symposium on Artificial Neural Networks (ESANN 2006 Proceedings), pp.323-330, 2006.

A. L. Casanovas, G. Monaci, and P. Vandergheynst, Blind Audiovisual Source Separation Based on Sparse Redundant Representations, IEEE Transactions on Multimedia, vol.12, issue.5, pp.358-371, 2010.
DOI : 10.1109/TMM.2010.2050650
URL : https://hal.archives-ouvertes.fr/inria-00541412

M. Kleinsteuber and S. Hao, Blind Source Separation With Compressively Sensed Linear Mixtures, IEEE Signal Processing Letters, vol.19, issue.2, pp.107-110, 2012.
DOI : 10.1109/LSP.2011.2181945
URL : http://arxiv.org/abs/1110.2593

J. S. Bendat and A. G. Piersol, Random Data: Analysis and Measurement Procedures, 1971.
DOI : 10.1002/9781118032428

D. Hallman and J. S. Bolton, Multi-reference near-field acoustical holography, Proceedings of Inter-noise'92, pp.1165-1170, 1992.

D. L. Hallman and J. S. Bolton, Comparison of multi-reference nearfield acoustical holography procedures, Proceedings of National Conference on Noise Control Engineering, pp.929-934, 1994.

J. F. Li, J. C. Pascal, and C. Carles, Reconstruction of partially coherent sources by use of principal component analysis, Proceedings of Inter-noise'95, pp.1355-1358, 1995.

R. J. Ruhala and C. B. Burroughs, Separation of leading edge, trailing edge, and sidewall noise sources from rolling tires, Proceedings of NOISE-CON 98, pp.109-114, 1998.

M. A. Tomlinson, Partial source discrimination in near field acoustic holography, Applied Acoustics, vol.57, issue.3, pp.243-261, 1999.
DOI : 10.1016/S0003-682X(98)00058-9

S. M. Price and R. J. Bernhard, Virtual coherence: A digital signal processing technique for incoherent source identification, Proceedings of the 4th International Modal Analysis Conference, pp.1256-1262, 1986.

K. U. Nam and Y. H. Kim, Visualization of multiple incoherent sources by the backward prediction of near-field acoustic holography, The Journal of the Acoustical Society of America, vol.109, issue.5, pp.1808-1816, 2001.
DOI : 10.1121/1.1358888

Y. J. Kim, J. S. Bolton, and H. S. Kwon, Partial sound field decomposition in multireference near-field acoustical holography by using optimally located virtual references, The Journal of the Acoustical Society of America, vol.115, issue.4, pp.1641-1652, 2004.
DOI : 10.1121/1.1642627

M. Y. Lee and J. S. Bolton, Scan-based near-field acoustical holography and partial field decomposition in the reference of noise and source level variation, pp.382-393, 2006.

K. U. Nam and Y. H. Kim, A partial field decomposition algorithm and its examples for near-field acoustic holography, The Journal of the Acoustical Society of America, vol.116, issue.1, pp.172-185, 2004.
DOI : 10.1121/1.1756896

Z. M. Zhong, J. Chen, and P. Zhong, Application of the blind source separation method to feature extraction of machine sound signals. Advanced Manufacturing Technology, pp.855-862, 2006.

K. Teramoto and M. T. Khan, Real???time acoustic blind signal separation system based on the spatio???temporal gradient analysis, The International Conference of Acoustics 08 Paris, pp.111-116, 2008.
DOI : 10.1121/1.2932891

R. Aichner, H. Buchner, and F. Yan, A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments, Signal Processing, vol.86, issue.6, pp.1260-1277, 2006.
DOI : 10.1016/j.sigpro.2005.06.022

M. Zibulevsky and B. A. Pearlmutter, Blind Source Separation by Sparse Decomposition in a Signal Dictionary, Neural Computation, vol.1, issue.4, pp.863-882, 2001.
DOI : 10.1016/S0042-6989(97)00169-7

E. Vincent, R. Gribonval, and C. Févotte, Performance measurement in blind audio source separation, IEEE Transactions on Audio, Speech and Language Processing, vol.14, issue.4, pp.1462-1469, 2006.
DOI : 10.1109/TSA.2005.858005
URL : https://hal.archives-ouvertes.fr/inria-00544230

B. H. Wu, G. P. Too, and S. Lee, Audio signal separation via a combination procedure of time-reversal and deconvolution process, Mechanical Systems and Signal Processing, vol.24, issue.5, pp.1431-1443, 2010.
DOI : 10.1016/j.ymssp.2009.11.008

E. Zhang, J. Antoni, and B. Dong, Bayesian space-frequency separation of wide-band sound sources by a hierarchical approach, The Journal of the Acoustical Society of America, vol.132, issue.5, pp.3240-3250, 2012.
DOI : 10.1121/1.4754530
URL : https://hal.archives-ouvertes.fr/hal-01018733

D. R. Brillinger, Time Series: Data Analysis and Theory, Philadelphia : SIAM, vol.540, 2001.
DOI : 10.1137/1.9780898719246

J. Antoni and S. Chauhan, A study and extension of second-order blind source separation to operational modal analysis. Sound and Vibration, pp.1079-1106, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00926071

P. A. Nelson and S. H. Yoon, ESTIMATION OF ACOUSTIC SOURCE STRENGTH BY INVERSE METHODS: PART I, CONDITIONING OF THE INVERSE PROBLEM, Journal of Sound and Vibration, vol.233, issue.4, pp.643-668, 2000.
DOI : 10.1006/jsvi.1999.2837

S. H. Yoon and P. A. Nelson, ESTIMATION OF ACOUSTIC SOURCE STRENGTH BY INVERSE METHODS: PART II, EXPERIMENTAL INVESTIGATION OF METHODS FOR CHOOSING REGULARIZATION PARAMETERS, Journal of Sound and Vibration, vol.233, issue.4, pp.669-705, 2000.
DOI : 10.1006/jsvi.2000.2836

P. A. Nelson, A review of some inverse problems in acoustics, Acoustics and Vibration, vol.6, pp.118-134, 2001.

Y. Kim and P. A. Nelson, Spatial resolution limits for the reconstruction of acoustic source strength by inverse methods. Sound and Vibration, pp.583-608, 2003.

Y. Kim and P. A. Nelson, Optimal regularization for acoustic source reconstruction by inverse methods. Sound and Vibration, pp.463-487, 2004.

E. G. Williams, Regularization methods for near-field acoustical holography, The Journal of the Acoustical Society of America, vol.110, issue.4, pp.1976-1988, 2001.
DOI : 10.1121/1.1404381

A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, Philadelphia : SIAM, vol.352, 2005.
DOI : 10.1137/1.9780898717921

J. Idier, Bayesian Approach to Inverse Problems, 2008.
DOI : 10.1002/9780470611197
URL : https://hal.archives-ouvertes.fr/hal-00400668

A. Kirsch, An introduction to the Mathematical Theory of Inverse Problems, 2011.

J. Hald, STFT ? A unique technique for scan-based near-field acoustic holography without restrictions on coherence, B & K Technical Review, vol.56, issue.1, 1989.

S. H. Yoon and P. A. Nelson, A method for the efficient construction of acoustic pressure cross-spectral matrices. Sound and Vibration, pp.897-920, 2000.

Y. Kim and P. A. Nelson, Estimations of acoustic source strength within a cylindrical duct by inverse problems. Sound and Vibration, pp.391-413, 2004.

P. A. Nelson and J. F. Rose, The time domain response of some systems for sound reproduction. Sound and Vibration, pp.461-493, 2006.

K. R. Holland and P. A. Nelson, An experimental comparison of the focused beamformer and the inverse method for the characterization of acoustic sources in ideal and non-ideal acoustic environments. Sound and Vibration, pp.4425-4437, 2012.

K. R. Holland and P. A. Nelson, The application of inverse methods to spatially-distributed acoustic sources. Sound and Vibration, pp.5727-5747, 2013.

J. B. Fahnline and G. H. Koopmann, A numerical solution for the general radiation problem based on the combined methods of superposition and singular???value decomposition, The Journal of the Acoustical Society of America, vol.90, issue.5, pp.2808-2819, 1991.
DOI : 10.1121/1.401878

A. D. Polyanin and A. V. Manzhirov, Handbook of integral equations, pp.573-706, 2008.

R. Pintelon and J. Schoukens, System identification: A frequency domain approach, 2012.

S. Choi, A. Cichocki, and H. M. Park, Blind source separation and independent component analysis : A Review, Neural Information Processing-Letters and Reviews, vol.6, pp.1-57, 2005.

J. Bobin, J. L. Starck, and Y. Moudden, Blind source separation: The sparsity revolution. Advanced in Imaging and Electron Physics, pp.221-302, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00252075

F. J. Theis, A. Jung, and C. G. Puntonet, Linear Geometric ICA: Fundamentals and Algorithms, Neural Computation, vol.23, issue.1, pp.419-439, 2003.
DOI : 10.1007/BF00205972
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.8118

C. Bi, X. Chen, and R. Zhou, Landweber iterative regularization for nearfield acoustic holography, Chinese Science Bulletin, vol.107, issue.6, pp.1374-1380, 2006.
DOI : 10.1007/s11434-006-1374-z

J. C. Pascal, S. Paillasseur, and J. H. Thomas, Patch near-field acoustic holography: Regularized extension and statistically optimized methods, The Journal of the Acoustical Society of America, vol.126, issue.3, pp.1264-1268, 2009.
DOI : 10.1121/1.3192349

S. Paillasseur, J. H. Thomas, and J. C. Pascal, Regularization for improving the deconvolution in real-time near-filed acoustic holography, pp.3777-3787, 2011.

F. J. Fahy, Sound Intensity, 1995.

L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis, 1991.

M. E. Tipping and C. M. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999.
DOI : 10.1111/1467-9868.00196

. Entropy, Available on : http://en.wikipedia.org/wiki, Consult at 15:03, on 23, 2013.

E. Shannon, A mathematical theory of communication. The Bell System Technical Journal, pp.379-423, 1948.

E. T. Jaynes, Information Theory and Statistical Mechanics, Physical Review, vol.106, issue.4, pp.620-630, 1957.
DOI : 10.1103/PhysRev.106.620

A. G. Wilson, Entropy in Urban and Regional Modelling, 1970.

N. G. Roegen, The Entropy Law and the Economic Process, p.p, 1971.

M. Batty, Spatial Entropy, Geographical Analysis, vol.11, issue.Supplement, pp.1-31, 1974.
DOI : 10.1111/j.1538-4632.1974.tb01014.x

M. Batty, Entropy in Spatial Aggregation, Geographical Analysis, vol.53, issue.Supplement, pp.1-21, 1976.
DOI : 10.1111/j.1538-4632.1976.tb00525.x

H. Krim and M. Viberg, Two decades of array signal processing research: the parametric approach, IEEE Signal Processing Magazine, vol.13, issue.4, pp.67-96, 1996.
DOI : 10.1109/79.526899

J. H. Manton, On the Role of Differential Geometry in Signal Processing, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.1021-1024
DOI : 10.1109/ICASSP.2005.1416480

J. H. Manton, Optimization algorithms exploiting unitary constraints, IEEE Transactions on Signal Processing, vol.50, issue.3, pp.635-650, 2002.
DOI : 10.1109/78.984753

T. E. Abrudan, J. Eriksson, and V. Koivunen, Optimization under Unitary Matrix Constraint using Approximate Matrix Exponential, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., pp.242-246, 2005.
DOI : 10.1109/ACSSC.2005.1599741

T. Abrudan, J. Eriksson, and V. Koivunen, Efficient Riemannian algorithms for optimization under unitary matrix constraint, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2353-2356, 2008.
DOI : 10.1109/ICASSP.2008.4518119

T. E. Abrudan, J. Eriksson, and V. Koivunen, Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint, IEEE Transactions on Signal Processing, vol.56, issue.3, pp.1134-1147, 2008.
DOI : 10.1109/TSP.2007.908999

T. Abrudan, J. Eriksson, and V. Koivunen, Conjugate gradient algorithm for optimization under unitary matrix constraint, Signal Processing, vol.89, issue.9, pp.1704-1714, 2009.
DOI : 10.1016/j.sigpro.2009.03.015

T. Abrudan, Advanced optimization algorithms for sensor arrays and multi-antenna communications Doctoral dissertation from Faculty of Electronics, pp.33-50, 2008.

T. Abrudan, J. Eriksson, and V. Koivunen, Efficient Line Search Methods for Riemannian Optimization Under Unitary Matrix Constraint, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, pp.671-675, 2007.
DOI : 10.1109/ACSSC.2007.4487298

D. H. Brandwood, A complex gradient operator and its application in adaptive array theory, IEE Proceedings F: Communications, Radar and Signal Processing, pp.11-16, 1983.

A. Hjørungnes and D. Gesbert, Introduction to complex-valued matrix differentiation. [Online] Available on

A. Hjørungnes and D. Gesbert, Complex-Valued Matrix Differentiation: Techniques and Key Results, IEEE Transactions on Signal Processing, vol.55, issue.6, pp.2740-2746, 2007.
DOI : 10.1109/TSP.2007.893762

A. Hjørungnes, D. Gesbert, and D. P. Palomar, Unified Theory of Complex-Valued Matrix Differentiation, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.345-348, 2007.
DOI : 10.1109/ICASSP.2007.366543

E. Ollila, V. Koivunen, and H. V. Poor, Complex-valued signal processing ? essential models, tools and statistics. Information Theory and Applications Workshop (ITA), pp.1-10, 2011.
DOI : 10.1109/ita.2011.5743596

A. Hjørungnes, Complex-Valued Matrix Derivatives: With Applications in Signal Processing and Communications, 2011.
DOI : 10.1017/CBO9780511921490

F. Fahy and P. Gardonio, Sound and Structural Vibration: Radiation, Transmission and Response, 2007.
DOI : 10.1115/1.3143810

D. B. Williams, Detection, Digital Signal Processing Handbook, pp.1436-1445, 1999.
DOI : 10.1201/9781420046052-c8
URL : https://hal.archives-ouvertes.fr/hal-01059901

M. S. Bartlett, A note on the multiplying factors for various ? 2 approximations, Royal Statistical Society, Series B: Methodological, vol.16, pp.296-298, 1954.

D. N. Lawley, TESTS OF SIGNIFICANCE FOR THE LATENT ROOTS OF COVARIANCE AND CORRELATION MATRICES, Biometrika, vol.43, issue.1-2, pp.128-136, 1956.
DOI : 10.1093/biomet/43.1-2.128

D. B. Williams and D. H. Johnson, Using the sphericity test for source detection with narrow-band passive arrays, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.38, issue.11, pp.2008-2014, 1990.
DOI : 10.1109/29.103104

M. Wax and T. Kailath, Detection of signals by information theoretic criteria, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.33, issue.2, pp.387-392, 1985.
DOI : 10.1109/TASSP.1985.1164557

H. Akaike, Information theory and extension of the maximum likelihood principle, the Second International Symposium on Information Theory, pp.267-281, 1973.

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

H. Akaike, Likelihood of a model and information criteria, Journal of Econometrics, vol.16, issue.1, pp.3-14, 1981.
DOI : 10.1016/0304-4076(81)90071-3

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, 1978.
DOI : 10.1214/aos/1176344136

J. Rissanen, Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978.
DOI : 10.1016/0005-1098(78)90005-5

J. Rissanen, A Universal Prior for Integers and Estimation by Minimum Description Length, The Annals of Statistics, vol.11, issue.2, pp.416-431, 1983.
DOI : 10.1214/aos/1176346150

L. C. Zhao, P. R. Krishnaiah, and Z. D. Bai, Remarks on certain criteria for detection of number of signals, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.35, issue.2, pp.129-132, 1987.
DOI : 10.1109/TASSP.1987.1165109

K. M. Wong, Q. T. Zhang, and J. P. Reilly, On information theoretic criteria for determining the number of signals in high resolution array processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.38, issue.11, pp.1959-1971, 1990.
DOI : 10.1109/29.103097

W. Xu and M. Kaveh, Analysis of the performance and sensitivity of eigendecomposition-based detectors, IEEE Transactions on Signal Processing, vol.43, pp.1413-1426, 1995.

A. P. Liavas and P. A. Regalia, On the behavior of information theoretic criteria for model order selection, IEEE Transactions on Signal Processing, vol.49, issue.8, pp.1689-1695, 2001.
DOI : 10.1109/78.934138
URL : https://hal.archives-ouvertes.fr/hal-01345076

E. Fishler, M. Grosmann, and H. Messer, Detection of signals by information theoretic criteria: general asymptotic performance analysis, IEEE Transactions on Signal Processing, vol.50, issue.5, pp.1027-1035, 2002.
DOI : 10.1109/78.995060

S. Kritchman and B. Nadler, Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory, IEEE Transactions on Signal Processing, vol.57, issue.10, pp.3930-3941, 2009.
DOI : 10.1109/TSP.2009.2022897

B. Nadler, Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator, IEEE Transactions on Signal Processing, vol.58, issue.5, pp.2746-2756, 2010.
DOI : 10.1109/TSP.2010.2042481

L. C. Zhao, P. R. Krishnaiah, and Z. D. Bai, On detection of the number of signals in presence of white noise, Journal of Multivariate Analysis, vol.20, issue.1, pp.1-25, 1986.
DOI : 10.1016/0047-259X(86)90017-5

D. B. Williams, Comparison of AIC and MDL to the minimum probability of error criterion, [1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing, pp.114-117, 1992.
DOI : 10.1109/SSAP.1992.246861

D. Otto, P. Sas, and P. Van-de-ponseels, Principal component analysis for noise source identification, Proceedings of the 6th IMAC, pp.1207-1214, 1988.

I. T. Jolliffe, Principal Component Analysis, 2002.
DOI : 10.1007/978-1-4757-1904-8

C. T. Latombe, Détection et caractérisation des signaux à plusieurs composants à partir de la matrice interspectrale (Detection and characterization of several-component signal from the interspectral matrix) Doctoral dissertation from, p.165, 1982.

M. S. Kompella, P. Davies, and R. J. Bernhard, A technique to determine the number of incoherent sources contributing to the response of a system, Mechanical Systems and Signal Processing, pp.363-380, 1994.
DOI : 10.1006/mssp.1994.1028

P. C. Hansen, Analysis of Discrete Ill-Posed Problems by Means of the L-Curve, SIAM Review, vol.34, issue.4, pp.561-580, 1992.
DOI : 10.1137/1034115

P. C. Hansen and D. P. Leary, The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems, SIAM Journal on Scientific Computing, vol.14, issue.6, pp.1487-1503, 1993.
DOI : 10.1137/0914086

P. C. Hansen, Rank-Deficient and Discrete Ill-Posed Problem: Numerical Aspects of Linear Inversion, Philadelphia : SIAM, vol.263, 1998.
DOI : 10.1137/1.9780898719697

S. Kullback, Information Theory and Statistics, 1997.

J. Hald, Array designs optimized for both low-frequency NAH and high-frequency Beamforming, The 33rd International Congress and Exposition on Noise Control Engineering, pp.1-8, 2004.
DOI : 10.4271/2005-08-0014

J. A. Raff and R. D. Perry, A review of vehicle noise studies carried out at the institute of sound and vibration research with a reference to some recent research on petrol engine noise, Journal of Sound and Vibration, vol.28, issue.3, pp.433-470, 1973.
DOI : 10.1016/S0022-460X(73)80034-3

R. J. Alfredson, The partial coherence technique for source identification on a Diesel engine. Sound and Vibration, pp.487-494, 1977.

J. Hald, Acoustical testing of a diesel engine using STFT, Brüel & Kjaer Application notes, vol.12, 1987.

M. F. Albright, Conditioned Source Analysis, A Technique for Multiple Input System Identification with Application to Combustion Energy Separation in Piston Engines, SAE Technical Paper Series, pp.1165-1177, 1995.
DOI : 10.4271/951376

F. J. Harris, On the use of windows for harmonic analysis with the discrete Fourier transform, Proceedings of the IEEE, pp.51-83, 1978.
DOI : 10.1109/PROC.1978.10837