The purpose of Blind Source Separation (BSS) is to obtain separated sources from convolutive mixture inputs. Among the various available BSS methods, Independent Component Analysis (ICA) is one of the representative methods. Its key idea is to repetitively update and calculate the measures. However, dealing with the measures obtained from multi-array sensors causes obstacles for real-time use. ...

*Supported through a Postdoctoral Research Fellowship awarded by the Royal Academy of Engineering of the UK. We approach the problem of blind identification and equalization (BIE) of single-user digital communication channels from the perspective of blind source separation (BSS). A new BSS-based BIE algorithm is proposed in this paper and is compared with a subspace method as well as a normaliz...

A speech enhancement scheme is presented integrating spatial and temporal signal processing methods for blind denoising in non stationary noise environments. In a first stage, spatially localized interferring point sources are separated from noisy speech signals recorded by two microphones using a Blind Source Separation (BSS) algorithm assuming no a priori knowledge about the sources involved....

Since the beginning of the last two decades, many researchers have been involved in the problem of Blind Source Separation (BSS). Whilst hundreds of algorithms have been proposed to solve BSS. These algorithms are well known as Independent Component Analysis (ICA) algorithms. Nowadays, ICA algorithms have been used to deal with various applications and they are using many performance indices. T...

In this paper, we consider the nonlinear Blind Source Separation BSS and independent component analysis (ICA) problems, and especially uniqueness issues, presenting some new results. A fundamental di6culty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they are nonunique without a suitable regularization. In this paper, we mainly discuss three di8erent ways f...

Reverberation is known to introduce difficulties in audio source separation, and reverse engineering independent sources from a convolutive mixture is one of the toughest challenges within blind source separation. This paper proposes two novel methods that combine dereverberation work with microphone interference reduction. The results are evaluated objectively using the BSS Eval toolbox and Re...

Blind source separation (BSS) techniques are widely used to extract signals of interest from a mixture with other signals, such as extracting fetal electrocardiogram (ECG) signals from noninvasive recordings on the maternal abdomen. These BSS techniques, however, typically lack possibilities to incorporate any prior knowledge on the mixing of the source signals. Particularly for fetal ECG signa...

This paper introduces the blind source separation (BSS) of convolutive mixtures of acoustic signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. Particular attent...

The Blind Source Separation (BSS) problem is often solved by maximizing objective functions reflecting the statistical dependency between outputs. Since global maximization may be difficult without exhaustive search, criteria for which it can be proved that all the local maxima correspond to an acceptable solution of the BSS problem have been developed. These criteria are used in a deflation pr...

Blind Source Separation (BSS) is an effective and powerful tool for source separation and artifact removal in EEG signals. For the real time applications such as Brain Computer Interface (BCI) or clinical Neuro-monitoring, it is of prime importance that BSS is effectively performed in real time. The motivation to implement BSS in Field Programmable Gate Array (FPGA) comes from the hypothesis th...