Learn More
—A maximum likelihood (ML) approach is used to separate the instantaneous mixtures of temporally correlated, independent sources with neither preliminary transformation nor a priori assumption about the probability distribution of the sources. A Markov model is used to represent the joint probability density of successive samples of each source. The joint(More)
—In the field of remote sensing, the unmixing of hyperspectral images is usually based on the use of a mixing model. Most existing spectral unmixing methods, used in the reflective range [0.4-2.5 µm], rely on a linear model of endmember reflectances. Nevertheless, such a model supposes the pixels at ground level to be uniformly irradiated and the scene to(More)
In this paper, we consider the nonlinear Blind Source Separation BSS and independent component analysis (ICA) problems, and especially uniqueness issues, presenting some new results. A fundamental diiculty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they are nonunique without a suitable regularization. In this paper,(More)
This letter presents new blind separation methods for moving average (MA) convolutive mixtures of independent MA processes. They consist of time-domain extensions of the FastICA algorithms developed by Hyvarinen and Oja for instantaneous mixtures. They perform a convolutive sphering in order to use parameter-free fast fixed-point algorithms associated with(More)
We proposed recently a new method for separating linear-quadratic mixtures of independent real sources, based on parametric identification of a recurrent separating structure using an ad hoc algorithm. In this paper, we develop a maximum likelihood approach providing an asymptotically efficient estimation of the model parameters. A major advantage of this(More)
Keywords: Independent component analysis Blind source separation Cramé r–Rao lower bound FastICA algorithm Piecewise stationary model a b s t r a c t We address independent component analysis (ICA) of piecewise stationary and non-Gaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The(More)
This paper concerns blind mixture identification (BMI) and blind source separation (BSS). We consider non-stationary stochastic sources, more specifically sources with slight time-domain sparsity. We first propose a correlation-based BMI/BSS method for Linear-Quadratic mixtures, called LQ-TEMPCORR. We also investigate the applicability of this type of(More)
We propose an extension of EFICA algorithm for piecewise stationary and non Gaussian signals. The proposed method is able to profit from varying distribution of the original signals and also from their varying variance, which is demonstrated by simulations with real-world signals. We show that in case of constant-variance signals, the accuracy of the method(More)
We recently proposed a markovian image separation method. The proposed algorithm is however very time consuming so that it cannot be applied to large-size real-world images. In this paper, we propose two major modifications i.e. utilization of a low-cost parametric score function estimator and derivation of a modified equivariant version of Newton-Raphson(More)