We introduce a new hybrid algorithm called the generalized sidelobe decorrelator (GSD) that combines elements of geometric beamforming and blind source separation. On the one hand, it is an extension of the generalized sidelobe canceller (GSC), also known as the Griffiths-Jim beamformer, from the standard criteria of power minimization to a decorrelation criteria. On the other hand, it can be s...

ABSTRACT Using algorithmic complexity to perform blind source separation (BSS) was first proposed by Pajunen. This approach presents the advantage of taking the whole signal structure into account to achieve separation, whereas standard ICA-based methods only use either time-correlations or higher order statistics in order to do so. Another advantage of this approach is that no assumptions abou...

In this paper, a novel method for linear normal mode identification based on Blind Source Separation (BSS) is introduced. Modal coordinates are considered as a specific case of sources that have certain time structure. This structure can be identified by many BSS algorithms. However, algorithms based on second order statistics are particularly suited for the linear normal mode identification. T...

The blind source separation problem (BSS) consists in extracting uncorrelated sources from an observed linear mixture. The rationale behind BSS is to reconstruct correctly the waveform of the signals allowing arbitrariness of their sign, order and energy. Known closed-from solutions succeed by relying on specific assumptions about the source distributional form. For more complex signal, such as...

We propose a new algorithm for blind source separation (BSS), in which frequency-domain independent component analysis (FDICA) and time-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, conventional TDICA fails to separate source signals under heavily reverberant conditions because of the low convergence in the ...

Blind source separation (BSS) aims to recover a set of statistically independent source signals from a set of linear mixtures of the same sources. In the noiseless real-mixture two-source two-sensor scenario, once the observations are whitened (decorrelated and normalized), only a Givens rotation matrix remains to be identified in order to achieve the source separation. In this paper, an adapti...

A new two-stage blind source separation (BSS) method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMOmodel-based signals from independen...

1. INTRODUCTION. Traditionally blind source separation (BSS) has often been considered as an inverse problem. In this paper we show that the theoretically optimum convolutive BSS solution corresponds to blind multiple-input multiple-output (MIMO) system identification. By choosing an appropriate filter length we show that for broadband algorithms the well-known ambiguities can be avoided. Ambig...

Methods for Blind Source Separation (BSS) aim at recovering signals from their mixture without prior knowledge about the signals and the mixing system. Among others, they provide tools for enhancing speech signals when they are disturbed by unknown noise or other interfering signals in the mixture. This paper considers a recent time-domain BSS method that is based on a complete decomposition of...

Blind Source Separation (BSS) consists in recovering unobserved signals from observed mixtures of them. In most cases the whole set of mixtures is used for the separation, possibly after a dimension reduction by PCA. This paper aims to show that in many applications the quality of the separation can be improved by first selecting a subset of some mixtures among the available ones, possibly by a...