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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)
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)
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)
Many source separation methods are restricted to non-Gaus-sian, stationary and independent sources. This yields some problems in real applications where the sources often do not match these hypotheses. Moreover, in some cases we are dealing with more sources than available observations which is critical for most classical source separation approaches. In(More)