For blind source separation, when the Fisher information matrix is used as the Riemannian metric tensor for the parameter space, the steepest descent algorithm to maximize the likelihood function in this Riemannian parameter space becomes the serial updating rule with equivariant property. This algorithm can be further simplified by using the asymptotic form of the Fisher information matrix aro...

Conventional blind source separation (BSS) algorithms are applicable when the number of sources equals to that of observations; however, they are inapplicable when the number of sources is larger than that of observations. Most underdetermined BSS algorithms have been developed based on an assumption that all sources have sparse distributions. These algorithms are applicable to separate speech ...

Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localizable in the time-frequency (t-f) domain. In this paper, we introduce a simple method for autoterm and crossterm selection, and propose the use of STFD matrices for both p...

This paper describes a method for solving the permutation problem in blind source separation (BSS) by frequencydomain independent component analysis (FD-ICA). FD-ICA is a well-known method for BSS of convolutive mixtures. However, FD-ICA has a source permutation problem, where estimated source components can become swapped at different frequencies. Many researchers have suggested methods to sol...

Usually, dam monitoring systems are based on both boundary conditions (temperature, rainfall, water level, etc.) and structural responses (displacements, rotations, pore pressures, etc.). Statistical analysis tools are widely used to compare the current response of the dam with a whole set of recorded data, in order to determine eventual unwanted behaviors. The main drawback of this approach is...

We relate information theoretic blind learning methods (infomax) and Bussgang blind equalization methods. The multipath extension of blind source separation methods can be seen in the frequency domain using FIR matrix algebra (matrices of nite impulse response lters). Three forms of Bussgang algorithms are given. The blind serial update method of Cardoso and Laheld is related to the infomax obj...

This paper presents a novel scheme for detection of watermarks embedded in multimedia signals using spread spectrum (SS) techniques. The detection method is centered on using the model that the embedded watermark and the host signal are mutually independent. The proposed detector assumes that the host signal and the watermark obey non-Gaussian distributions. The proposed blind watermark detecto...

The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsification, which allows ...

Overcomplete blind source separation (BSS) tries to recover more sources from less sensor signals. We present a new approach based on an estimated histogram of the sensor data; we search for the points fulfilling the overcomplete Geometric Convergence Condition, which has been shown to be a limit condition of overcomplete geometric BSS [1]. The paper concludes with an example and a comparison o...