This paper presents a PDF-matched modification to Stone’s measure of predictability. The modified measure of predictability is a measure of non-gaussianity too. It is an extent of signal predictability by two different prediction terms. One prediction term is based on a normal gaussian PDF assumption for signal. In contrast, the other one is based on a unit variance supergaussian PDF assumption...

Unsupervised clustering algorithms can be combined to improve the robustness and the quality of the results, e.g. in blind source separation. Before combining the results of these clustering methods the corresponding clusters have to be aligned, but usually it is not known which clusters of the employed methods correspond to each other. In this paper, we present a method to avoid this correspon...

Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evalu...

Large sets of multivariate measurement data are now routinely available through automated in-process measurement in many manufacturing industries. These data typically contain valuable information regarding the nature of each major source of process variability. In this article we assume that each variation source causes a distinct spatial variation pattern in the measurement data. The model th...

In this paper, we consider independence property between a random process and its first derivative. Then, for linear mixtures, we show that cross-correlations between mixtures and their derivatives provide a sufficient number of equations for analytically computing the unknown mixing matrix. In addition to its simplicity, the method is able to separate Gaussian sources, since it only requires s...

In this paper we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals in contrast to most other major approaches to this problem. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the time-frequency domain. We show some results of experiments with both artificially controlled data and speech d...

Looking at the speaker s face is useful to hear better a speech signal and extract it from competing sources before identification. This might result in elaborating new speech enhancement or extraction techniques exploiting the audiovisual coherence of speech stimuli. In this paper, a novel algorithm plugging audio-visual coherence estimated by statistical tools on classical blind source separa...

This note illustrates some shortcomings of the criterion proposed in “Blind source separation based on endpoint estimation with applications to the MLSP 2006 data competition” when the number of samples is finite. This algorithm considers mutually independent sources with semibounded support, however, even for a sufficient sample size for which the finite bound of the support of the density of ...

Most of the HO blind source separation methods developed this last decade aim at blindly separating statistically independent sources, assumed stationary and ergodic. Nevertheless, in many situations such as in radiocommunications, the sources are non stationary and very often (quasi)-cyclostationary (digital modulations). In these contexts, it is important to wonder if the performance of these...