A new convolutive source separation approach for independent/dependent source components
Tài liệu tham khảo
Hansen, 2003, Ica if fmri based on a convolutive mixture model
Anemüller, 2003, Complex independent component analysis of frequency-domain electroencephalographic data, Neural Netw., 16, 1311, 10.1016/j.neunet.2003.08.003
Cruces-Alvarez, 2000, An iterative inversion approach to blind source separation, IEEE Trans. Neural Netw., 11, 1423, 10.1109/72.883471
Mansour, 2006, Blind separation of underwater acoustic signals, 181
Haykin, 2005, The cocktail party problem, Neural Comput., 17, 1875, 10.1162/0899766054322964
Nuzillard, 2000, Blind source separation and analysis of multispectral astronomical images, Astron. Astrophys. Suppl. Ser., 147, 129, 10.1051/aas:2000292
Dyrholm, 2006, Model structure selection in convolutive mixtures, 74
Babaie-Zadeh, 2005, A general approach for mutual information minimization and its application to blind source separation, Signal Process., 85, 975, 10.1016/j.sigpro.2004.11.021
Castella, 2011, New kurtosis optimization schemes for miso equalization, IEEE Trans. Signal Process., 60, 1319, 10.1109/TSP.2011.2177828
Yellin, 1994, Criteria for multichannel signal separation, IEEE Trans. Signal Process., 42, 2158, 10.1109/78.301850
Thi, 1995, Blind source separation for convolutive mixtures, Signal Process., 45, 209, 10.1016/0165-1684(95)00052-F
Bell, 1995, An information-maximization approach to blind separation and blind deconvolution, Neural Comput., 7, 1129, 10.1162/neco.1995.7.6.1129
Comon, 1994, Independent component analysis, a new concept?, Signal Process., 36, 287, 10.1016/0165-1684(94)90029-9
Hyvärinen, 2000, Independent component analysis: algorithms and applications, Neural Netw., 13, 411, 10.1016/S0893-6080(00)00026-5
Saito, 2015, Convolutive blind source separation using an iterative least-squares algorithm for non-orthogonal approximate joint diagonalization, IEEE/ACM Trans. Audio Speech Lang. Process., 23, 2434, 10.1109/TASLP.2015.2485663
Smaragdis, 1998, Blind separation of convolved mixtures in the frequency domain, Neurocomputing, 22, 21, 10.1016/S0925-2312(98)00047-2
Makino, 2005, Blind source separation of convolutive mixtures of speech in frequency domain, IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 88, 1640, 10.1093/ietfec/e88-a.7.1640
Hyvärinen, 2001, Topographic independent component analysis, Neural Comput., 13, 1527, 10.1162/089976601750264992
Hyvärinen, 2004, Blind separation of sources that have spatiotemporal variance dependencies, Signal Process., 84, 247, 10.1016/j.sigpro.2003.10.010
Caiafa, 2006, Separation of statistically dependent sources using an l2-distance non-Gaussianity measure, Signal Process., 86, 3404, 10.1016/j.sigpro.2006.02.032
Caiafa, 2012, On the conditions for valid objective functions in blind separation of independent and dependent sources, EURASIP J. Adv. Signal Process., 2012, 255, 10.1186/1687-6180-2012-255
Theis, 2008, A robust model for spatiotemporal dependencies, Neurocomputing, 71, 2209, 10.1016/j.neucom.2007.06.012
Theis, 2005, Blind signal separation into groups of dependent signals using joint block diagonalization, 5878
Boudjellal, 2014, Separation of dependent autoregressive sources using joint matrix diagonalization, IEEE Signal Process. Lett., 22, 1180, 10.1109/LSP.2014.2380312
Caiafa, 2006, A minimax entropy method for blind separation of dependent components in astrophysical images, 81
Caiafa, 2013, Using generic order moments for separation of dependent sources with linear conditional expectations, 1
Kuruoglu, 2013, Dependent component analysis, EURASIP J. Adv. Signal Process., 185
Bedini, 2004, Separation of dependent sources in astrophysical radiation maps using second order statistics
Castella, 2013, Separation of instantaneous mixtures of a particular set of dependent sources using classical ica methods, EURASIP J. Adv. Signal Process., 2013, 62, 10.1186/1687-6180-2013-62
Kuruoglu, 2010, Dependent component analysis for cosmology: a case study, 538
Xiang, 2015
Erdogan, 2013, A class of bounded component analysis algorithms for the separation of both independent and dependent sources, IEEE Trans. Signal Process., 61, 5730, 10.1109/TSP.2013.2280115
Inan, 2015, Convolutive bounded component analysis algorithms for independent and dependent source separation, IEEE Trans. Neural Netw. Learn. Syst., 26, 697, 10.1109/TNNLS.2014.2320817
Inan, 2015, A convolutive bounded component analysis framework for potentially nonstationary independent and/or dependent sources, IEEE Trans. Signal Process., 63, 18, 10.1109/TSP.2014.2367472
Keziou, 2014, New blind source separation method of independent/dependent sources, Signal Process., 104, 319, 10.1016/j.sigpro.2014.04.017
Ghazdali, 2017, Blind noisy mixture separation for independent/dependent sources through a regularized criterion on copulas, Signal Process., 131, 502, 10.1016/j.sigpro.2016.09.006
Simon, 1999
Sklar, 1959, Fonctions de répartition à n dimensions et leurs marges, Publ. Inst. Stat. Univ. Paris, 8, 229
Nelsen, 2007
Joe, 1997
Babaie-Zadeh, 2001, Separating convolutive mixtures by mutual information minimization, 834
Silverman, 1986, Density Estimation for Statistics and Data Analysis, 10.1007/978-1-4899-3324-9
Chen, 2005, Pseudo-likelihood ratio tests for semiparametric multivariate copula model selection, Can. J. Stat., 33, 389, 10.1002/cjs.5540330306
Genest, 1995, A semiparametric estimation procedure of dependence parameters in multivariate families of distributions, Biometrika, 82, 543, 10.1093/biomet/82.3.543
Tsukahara, 2005, Semiparametric estimation in copula models, Can. J. Stat., 33, 357, 10.1002/cjs.5540330304
Bouzebda, 2010, New estimates and tests of independence in semiparametric copula models, Kybernetika, 46, 178
Boyd, 2003, Subgradient methods, lecture notes of EE392o, 2004