121,883 research outputs found

    Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization

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    Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to Blind Source Separation (BSS) problems. However, the separation results are sensitive to the initialization of parameters of NMF. Avoiding the subjectivity of choosing parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step algorithm in order to solve the underdetermined blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms

    A geometrically constrained multimodal time domain approach for convolutive blind source separation

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    A novel time domain constrained multimodal approach for convolutive blind source separation is presented which incorporates geometrical 3-D cordinates of both the speakers and the microphones. The semi-blind separation is performed in time domain and the constraints are incorporated through an alternative least squares optimization. Orthogonal source model and gradient based optimization concepts have been used to construct and estimate the model parameters which fits the convolutive mixture signals. Moreover, the majorization concept has been used to incorporate the geometrical information for estimating the mixing channels for different time lags. The separation results show a considerable improvement over time domain convolutive blind source separation systems. Having diagonal or quasi diagonal covariance matrices for different source segments and also having independent profiles for different sources (which implies nonstationarity of the sources) are the requirements for our method. We evaluated the method using synthetically mixed real signals. The results show high capability of the method for separating speech signals. © 2011 EURASIP

    A Semi-Blind Source Separation Method for Differential Optical Absorption Spectroscopy of Atmospheric Gas Mixtures

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    Differential optical absorption spectroscopy (DOAS) is a powerful tool for detecting and quantifying trace gases in atmospheric chemistry \cite{Platt_Stutz08}. DOAS spectra consist of a linear combination of complex multi-peak multi-scale structures. Most DOAS analysis routines in use today are based on least squares techniques, for example, the approach developed in the 1970s uses polynomial fits to remove a slowly varying background, and known reference spectra to retrieve the identity and concentrations of reference gases. An open problem is to identify unknown gases in the fitting residuals for complex atmospheric mixtures. In this work, we develop a novel three step semi-blind source separation method. The first step uses a multi-resolution analysis to remove the slow-varying and fast-varying components in the DOAS spectral data matrix XX. The second step decomposes the preprocessed data X^\hat{X} in the first step into a linear combination of the reference spectra plus a remainder, or X^=AS+R\hat{X} = A\,S + R, where columns of matrix AA are known reference spectra, and the matrix SS contains the unknown non-negative coefficients that are proportional to concentration. The second step is realized by a convex minimization problem S=argminnorm(X^AS)S = \mathrm{arg} \min \mathrm{norm}\,(\hat{X} - A\,S), where the norm is a hybrid 1/2\ell_1/\ell_2 norm (Huber estimator) that helps to maintain the non-negativity of SS. The third step performs a blind independent component analysis of the remainder matrix RR to extract remnant gas components. We first illustrate the proposed method in processing a set of DOAS experimental data by a satisfactory blind extraction of an a-priori unknown trace gas (ozone) from the remainder matrix. Numerical results also show that the method can identify multiple trace gases from the residuals.Comment: submitted to Journal of Scientific Computin

    Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability

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    Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no more valid in the presence of intra-class variabilities due to illumination conditions, weathering, slight variations of the pure materials, etc... In this paper, we first describe the results of investigations highlighting intra-class variability measured in real images. Considering these results, a new formulation of the linear mixing model is presented leading to two new methods. Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability. To overcome UP-NMF limitations an extended method is proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each sensed spectrum, these extended versions of NMF extract a corresponding set of source spectra. A constraint is set to limit the spreading of each source's estimates in IP-NMF. The methods are tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and then numerically mixed. We thus demonstrate the interest of our methods for realistic source variabilities. Finally, IP-NMF is tested on a real data set and it is shown to yield better performance than state of the art methods

    Semi-Blind Cancellation of IQ-Imbalances

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    International audienceThe technical realization of modern wireless receivers yields significant interfering IQ-imbalances, which have to be compensated digitally. To cancel these IQ-imbalances, we propose an algorithm using iterative blind source separation (IBSS) as well as information about the modulation scheme used (hence the term semi-blind). The novelty of our approach lies in the fact that we match the nonlinearity involved in the IBSS algorithm to the probability density function of the source signals. Moreover, we use approximations of the ideal non-linearity to achieve low computational complexity. For severe IQ-mismatch, the algorithm leads to 0.2 dB insertion loss in an AWGN channel and with 16-QAM modulation

    MULTI-SPECTRAL IMAGE ANALYSIS FOR SKIN PIGMENTATION CLASSIFICATION

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    International audienceIn this paper, we compare two different approaches for semi- automatic detection of skin hyper-pigmentation on multi- spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation
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