In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive projection algorithm (SPA), but takes advantage of the nonnegativity constraint ...

This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method witho...

In this paper we present results on single channel blind source separation based on a shift-invariant sparse coding model [1], [2] and [3]. This model learns a set of time-domain features from a single observation of the mixed signals. The found features can often be associated with a single source and can therefore be used to reconstruct the individual source signals. This is shown in this pap...

The research reported in this paper addresses the fundamental task of separation of locally moving or deforming image areas from a static or globally moving background. It builds on the latest developments in the field of robust principal component analysis, specifically, the recently reported practical solutions for the long-standing problem of recovering the low-rank and sparse parts of a lar...

A novel process for obtaining controlled morphologies in polymer blends is modeled numerically. Particles of one type of polymer are allowed to dissolve in a matrix of a dissimilar polymer. Prior to complete dissolution the blend is quenched into the two-phase region, such that phase separation takes place. The combination of the incomplete dissolution and the wavelength selection process assoc...

For many signal sources such as speech with distinct, nonwhite power spectral densities, second-order statistics of the received signal mixture can be exploited for signal separation. Without knowledge on noise correlation matrix, we propose a simple and yet effective signal extraction method for signal source separation under unknown temporally white noise. This new and unbiased signal extract...

The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsification, which allows ...

We consider the underdetermined blind source separation problem with linear instantaneous and convolutive mixtures when the input signals are sparse, or have been rendered sparse. In the underdetermined case the problem requires solving three subproblems: detecting the number of sources, estimating the mixing matrix, and finding an adequate inversion strategy to obtain the sources. This paper s...

In this paper, we address the problem of underdetermined Blind Source Separation (BSS) of anechoic speech mixtures. We propose a demixing algorithm that exploits the sparsity of certain time-frequency expansions of speech signals. Our algorithm merges l-basis-pursuit with ideas based on the degenerate unmixing estimation technique (DUET) [1]. There are two main novel components to our approach:...

In this paper, we present an efficient real-time implementation of a broadband algorithm for blind source separation (BSS) of convolutive mixtures. A recently introduced generic BSS framework based on a matrix formulation allows simultaneous exploitation of nonwhiteness and nonstationarity of the source signals using second-order statistics. We demonstrate here that this general scheme leads to...