We present a two stage blind source separation (BSS) algorithm for robot audition. The algorithm is based on a beamforming preprocessing and a BSS algorithm using a sparsity separation criterion. Before the BSS step, we filter the sensors outputs by beamforming filters to reduce the reverberation and the environmental noise. As we are in a robot audition context, the manifold of the sensor arra...

Blind source separation (BSS) problems have recently become an active research area in both statistical signal processing and unsupervised neural learning. In most approaches, the number of source signals is typically assumed to be known a priori, but this does not usually hold in practical applications. Although the problem of determining the unknown source number has been studied recently, th...

Among potential applications of blind source separation (BSS) a most promising one might be separation of speech signals. In real-world situations, however, any BSS algorithm for sound signals suffers from a difficulty. From a practical point of view the microphone array should be made compact, but then the mixing matrix becomes almost singular, inducing certain instability in the algorithm exe...

The goal of this paper is to review the multiresolution subband Blind Source Separation (MRSBSS) methods, classify them into different categories, and identify new trends. After the perspective of MRSBSS is briefly presented, the general and detailed definition of the MSBSS model is given, the relationships among BSS, Independent Component Analysis (ICA) and MRSBSS method are discussed simultan...

This paper presents a novel blind signal separation (BSS) approach based on the theory of independent component analysis. In the proposed BSS approach, the learning rule is derived by the conjugate gradient optimization algorithm rather than the ordinary gradient and natural gradient algorithm based on the minimum mutual information (MMI) criterion. The score function is a key point in solving ...

Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localizable in the time-frequency (t-f) domain. In this paper, we introduce a simple method for autoterm and crossterm selection, and propose the use of STFD matrices for both p...

Blind Source Separation (BSS) is a model free source separation technique which decomposes observed mixture data into mixing matrix and source matrix both of which are unknown beforehand. One well known BSS algorithm is joint diagonalization which is from the algebraic class and in which mixing structures are recovered by jointly diagonalizing the source condition matrix. In this study we first...

In order to resolve engineering problems that the performance of traditional blind source separation (BSS) methods deteriorates or even becomes invalid when unknown signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF...

Multiuser Processing refers to a set of fundamental techniques for the improvement of the performances of the modern wireless communications systems. Most of these techniques have been proposed and studied for specific scenarios, according to the multiple access technology to be employed. The theory and methods of blind source separation (BSS) can provide a more general framework over which mul...

Blind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. Th...