Blind signal separation (BSS) is an emerging signal processing technique, aiming at recovering unobserved signals or`sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but requires to venture beyond familiar second order statistics....

The problem of the blind signal separation (BSS) consists of estimating the latent component signals in a linear mixture, referred to as the sources, starting from several observed signals, without relying on any specific knowledge of the sources. In particular, when the sources are audible, this problem is known as to the cocktail-party problem, making reference to the ability of the human ear...

We present a quasi-maximum likelihood approach to blind source separation (BSS) which is based on approximating the source distributions by their truncated Edgeworth expansions. The paper focuses on the 22 case, for which the problem is known to reduce to the estimation of a single rotation angle. Unlike existing maximum likelihood BSS techniques , the proposed algorithm is consistent for any s...

In this paper, we propose a new method for evaluating the separation and dereverberation performance of a convolutive blind source separation (BSS) system, and investigate a separating system obtained by employing frequency domain BSS based on independent component analysis (ICA). As a result, we reveal the acoustical characteristics of the frequency domain BSS for convolutive mixture of speech...

Blind equalization of single input multiple output (SIMO) FIR channels can be reformulated as the problem of blind source separation. It was shown in [4] that the natural gradient-based source separation method could recover source successfully for ill-conditioned channels since it has the equivariant property. However, the e ect of additive noise was not considered in [4]. In this letter we de...

The present study carries out output-only modal analysis using two blind source separation (BSS) techniques, namely independent component analysis and second-order blind identification. The concept of virtual source is exploited and renders the application of these BSS techniques possible. The proposed modal analysis method is illustrated using numerical and experimental examples. r 2007 Elsevi...

Blind source separation (BSS) is a method for recovering a set of source signals from the observation of their mixtures without any prior knowledge about the mixing process. In BSS the definition of a source signal has an inherent indeterminacy; any linear transform of a source signal can also be considered a source signal. Due to this indeterminacy, there are an infinite number of valid separa...

Blind Source Separation (BSS) refers to the process of recovering source signals from a given mixture of unknown source signals were in no prior information about source and mixing methodology is known. Independent Component Analysis (ICA) is widely used BSS technique which allows separation of source components from complex mixture of signals based on certain statistical assumptions. This pape...

Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signals, the separation performance is still not enough. In particular, when the length of an impulse response is long, the performance is highly limited. In this paper, we consider the reason for the poor performance of BSS in a long reverberation environment. First, we show that it is useless to b...