Sharif Digital Repository

In this paper a new geometrical approach for separating speech signals is presented. This approach can be directly applied to separate more than two speech signals. It is based on clustering the observation points, and then fitting a line (hyper-plane) onto each cluster. The algorithm quality is shown to be improved by using DCT coefficients of speech signals, as opposed to using speech samples. © Springer-Verlag 2004  

In this letter, we compute the variation of the mutual information, resulting from a small variation in its argument. Although the result can be applied in many problems, we consider only one example: the result is used for deriving a new method for blind source separation in linear mixtures. The experimental results emphasize the performance of the resulting algorithm  

Digital documents are usually degraded during the scanning process due to the contents of the backside of the scanned manuscript. This is often caused by the show-through effect, i. e. the backside image that interferes with the main front side picture due to the intrinsic transparency of the paper. This phenomenon is one of the degradations that one would like to remove especially in the field of Optical Character Recognition (OCR) or document digitalization which require denoised texts as inputs. In this paper, we first propose a novel and general nonlinear model for canceling the show-through phenomenon. A nonlinear blind source separation algorithm is used for this purpose based on a new... 

Digital documents are usually degraded during the scanning process due to the printings existing on the backside of the scanning manuscript. This is often caused by the so called show-through effect, the image that interferes with the main picture due to the intrinsic opacity of the paper. This phenomenon is on type of degradation that one would like to remove. In this paper, we propose a novel and general nonlinear model for show-through phenomenon. A nonlinear blind source separation (BSS) algorithm is used for this particular application in a new recursive and extendible structure for compensating show-through. Finally, we introduce a new structure for removing the show-through and the... 

In this paper, a proof is provided to show that Gaussian signals will lose their Gaussianity if they are passed through a polynomial of an order greater than 1. This can help in blind compensation of polynomial nonlinearities on Gaussian sources by forcing the output to follow a Gaussian distribution (the term 'blind' refers to lack of any prior information about the nonlinear function). It may have many applications in different fields of nonlinear signal processing for removing the nonlinearity. Particularly, in nonlinear blind source separation, it can be used as a pre-processing step to transform the problem to a linear one, which is already well studied in the literature. This idea is... 

Using Mutual Information (MI) minimization is very common in Blind Source Separation (BSS). However, it is known that gradient descent approaches may trap in local minima of MI in constrained models. In this paper, it is proposed that this problem may be solved using a 'poor' estimation of the derivative of MI  

In this paper, an adaptive algorithm for blind source separation in linear instantaneous mixtures is proposed, and it is shown to be the optimum version of the EASI algorithm. The algorithm is based on minimization of mutual information of outputs. This minimization is done using adaptive estimation of a recently proposed non-parametric "gradient" for mutual information. © Springer-Verlag 2004  

Blind Source Separation (BSS) is a basic problem in signal processing. In this paper, we present a new method for separating convolutive mixtures based on the minimization of the output mutual information. We also introduce the concept of joint score function, and derive its relationship with marginal score function and independence. The new approach for minimizing the mutual information is very efficient, although limited by multivariate distribution estimations. © Springer-Verlag Berlin Heidelberg 2001  

The electrooculogram (EOG) artifact is one of the main contaminators of electroencephalographic recording (EEG). EOG can make serious problems in results and interpretations of EEG processing. Rejecting contaminated EEG segments result in an unacceptable data loss. Many methods were proposed to correct EOG artifact mainly based on regression and blind source separation (BSS). In this study, we proposed an automatic correction method based on maximum likelihood estimation. The proposed method was applied to our simulated data (real artifact free EEG plus controlled EOG) and results show that this method gives superior performance to Schlögl and SOBI methods  

Blind Source Separation (BSS) is the problem of separating signals which are mixed through an unknown function from a number of observations, without any information about the mixing model. Although it has been mathematically proven that the separation can be done when the mixture is linear, there is not any proof for the separability of nonlinearly mixed signals. Our contribution in this paper is performing nonlinear BSS for sparse sources. It is shown in this case, sources are separable even if the problem is under-determined (the number of observations is less than the number of source signals). However in the most general case (when the nonlinear mixing model can be of any kind and there... 

Detection of seizure periods in an epileptic patient is an important part of health care. However, due to the variety in types of seizures and location of them, real-time seizure detection is not straight forward. In this paper, we propose a method for seizure detection from EEG signals in datasets which have both generalized and focal seizures. The proposed method is useful in the situations that we have no prior knowledge about the location of the patient's seizure and the pattern of evolution of seizure location. In the proposed method, first, the artifacts are automatically reduced by Blind Source Separation (BSS) methods. Then, the channels are clustered into two clusters. After that,... 

Scanning process usually degrades digital documents due to the contents of the backside of the scanned manuscript. This is often because of the show-through effect, i.e. the backside image that interferes with the main front side picture mainly due to the intrinsic transparency of the paper used for printing or writing. In this paper, we first use one of Non-negative Matrix Factorization (NMF) methods for canceling show-through phenomenon. Then, non-linearity of show-through effect is included by changing the cost function used in this method. Simulation results show that this proposed algorithm can remove show-through effectively  

In this paper an approach for blind source separation in bilinear (or linear quadratic) mixtures is presented. The proposed algorithm employs the same recurrent structure as [Hosseini and Deville, 2003) for separating these mixtures . However, instead of maximal likelihood, our algorithm is based on minimizing the mutual information of the outputs for recovering the independent components. Simulation results show the efficiency of the proposed algorithm. © 2009 IEEE  

In this paper, a novel approach for performing blind source separation (BSS) in nonlinear mixtures is proposed, and their separability is studied. It is shown that this problem can be solved under a few assumptions, which are satisfied in most practical applications. The main idea can be considered as transforming a time-invariant nonlinear BSS problem to local linear ones varying along the time, using the derivatives of both sources and observations. Taking into account the proposed idea, numerous algorithms can be developed performing the separation. In this regard, an algorithm, supported by simulation results, is also proposed in this paper. It can be seen that the algorithm well... 

When our sources are graph signals, a more efficient algorithm for Blind Source Separation (BSS) can be provided by using structural graph information along with statistical independence and/or non-Gaussianity. To the best of our knowledge, the GraphJADE and GraDe algorithms are the only BSS methods addressing this issue in the case of known underlying graphs. However, in many real-world applications, these graphs are not necessarily a priori known. In this paper, we propose a method called GraphJADE-GL (GraphJADE with Graph Learning) that jointly separates the graph signal sources and learns the graphs related to them accurately, in an alternating style. © 1994-2012 IEEE  

Score Function Difference (SFD) is a recently proposed "gradient" for mutual information which can be used in Blind Source Separation algorithms based on minimization of mutual information. To be applied to practical problems, SFD must be estimated from the data samples. In this paper, a new method for estimating SFD is proposed. To compare the performance of this new estimator with other proposed SFD estimation methods, we have applied them in separating linear instantaneous mixtures. It will be seen that our method performs superior to all other methods previously proposed for estimation of SFD  

In this paper, a new algorithm for blind source separation in convolutive mixtures, based on minimizing the mutual information of the outputs, is proposed. This minimization is done using a recently proposed Minimization-Projection (MP) approach for minimizing mutual information in a parametric model. Since the minimization step of the MP approach is proved to have no local minimum, it is expected that this new algorithm has good convergence behaviours