Blind source separation (BSS) algorithms are unsupervised methods, which the cornerstone of hyperspectral data analysis by allowing for physically meaningful decompositions. BSS problems being ill-posed, resolution requires efficient regularization schemes to better distinguish between sources and yield interpretable solutions. For that purpose, we investigate a semi-supervised approach in comb...

Some research on sound source estimation by blind source separation (BSS) have been achieved and been satisfactory. On the aspect of sound source identification of rotating machine, some research is been achieved too. How to apply blind source separation to fault character identification and improve veracity of fault diagnosis is the research content of this paper. Based on the principle of min...

Blind source separation and blind output decorrelation are two well-known problems in signal processing. For instantaneous mixtures, blind source separation is equivalent to a generalized eigen-decomposition, while blind output decorrelation can be considered as an iterative method of output orthogonalization. We propose a steepest descent procedure on a new cost function based on the Frobenius...

The Infomax algorithm is a popular method in blind source separation problem. In this article an extension of the Infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-Gaussian distributions. This ability is the results of using two different nonlinear functions and new coefficients in the learning rule. In this paper we show how we can use the distribution...

A well-known fact in blind deconvolution is that if the unknown source signal is white (temporally) and the unknown channel filter is minimum phase, it is possible to determine the inverse filter (equalizer) by evaluating simply the power spectral density (PSD) of the received signal. For blind source separation, however, a similar special case, equivalent to the situation in blind deconvolutio...