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In this paper, we first briefly recall the principles of the " TIme-Frequency Ratio Of Mixtures " (TIFROM) approach that we recently proposed. We then show that, unlike Independent Component Analysis (ICA) methods, our approach can separate dependent signals, provided there exist some areas in the time-frequency plane where only one source occurs. We(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)
This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent structure adapted by a generic rule involving arbitrary separating functions. These functions should ideally be set so as to minimize the asymptotic error variance of the structure. However, these optimal functions are often unknown in practice.(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)
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)
In this paper, we propose two versions of a correlation-based blind source separation (BSS) method. Whereas its basic version operates in the time domain, its extended form is based on the time-frequency (TF) representations of the observed signals and thus applies to much more general conditions. The latter approach consists in identifying the columns of(More)