Yannick Deville

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Many source separation methods are restricted to non-Gaussian, 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)
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 be(More)
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 achieve(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 timefrequency (TF) representations of the observed signals and thus applies to much more general conditions. The latter approach consists in identifying the columns of(More)
We proposed recently a new method for separating linearquadratic 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)
The content of set associative and fully associative structures (such as cache memories, TLBs and main memories) is controlled by a replacement algorithm. Replacing the elements that have not been accessed for a long period yields high performance. This property has been used in the LRU policy, it is also used in this paper, in order to define a new class(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)