For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target...

In many applications of Independent Component Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the separating matrix. In this paper we present several algorithms ...

Blind source separation (BSS) and related methods, e.g., independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. The recent trends in blind source separation and generalized component analysis (GCA) is to consider problems in the framework of matrix factoriz...

Time-frequency domain blind source separation (BSS) leads to an important problem that generally the independence assumption between source signals collapses in frequency domain due to inadequate samples. It consequently degrades the performance of all the ICA-based BSS methods. To remedy the defect, we propose introducing the beamforming into the conventional BSS system taking the advantage th...

This study reviews the originality and development of the Brain Computer Interface (BCI) system and focus on the BCI system design based on Blind Source Separation (BSS) techniques. The study also provides the recent trends and discusses some of a new ideas for BSS techniques in BCI architecture, articles which discussing the BCI system development were analysed, types of the BCI systems and th...

Blind source separation (BSS) and related topics such as independent component analysis (ICA), sparse component analysis (SCA), or nonnegative matrix factorization (NMF) have become emerging tools inmultivariate signal processing and data analysis and are now one of the hottest and emerging areas in signal processing with solid theoretical foundations and many potential applications. In fact, B...

In this paper we investigate the application of Blind Source Separation (BSS) algorithms for the decoding of linearly precoded MIMO communication systems and for the design of limited feedback channels that send the Channel Status Information (CSI) from the receiver to the transmitter. The advantage of using BSS is that the MIMO channel can be continuously tracked without the need of pilot symb...

The problem of multiuser interference cancellation in wireless cellular communication systems accepts a blind source separation (BSS) model. The present contribution studies the closed-form solutions to BSS in the real-mixture case. Connections among a number of seemingly disparate methods are unveiled, new procedures are put forward, and their asymptotic (large-sample) performance is analyzed....

Blind source separation (BSS) refers to a wide class of methods in signal and image processing, which extract the underlying sources from a set of mixtures without almost any prior knowledge about the sources nor about the mixing process. In biomedical applications, BSS is used for the analysis of electroencephalogram (EEG), magenetoencephalogram (MEG) and electrocardiogram (ECG) signals and fu...

Over the last few years, the development of multi-channel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-called blind source separation (BSS) problem. In this context, as clearly emphasized by previous work, it is fundamental that the sources to be retrieved pr...