Andrzej CICHOCKI Pando GEORGIEV
In many applications of Independent Component Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the separating matrix. In this paper we present several algorithms and overview some known transformations which allows us to preserve several important constraints.
Seungjin CHOI Andrzej CICHOCKI Liqing ZHANG Shun-ichi AMARI
This paper addresses a maximum likelihood method for source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We consider an approximate likelihood which is based on the Laplace approximation and develop a natural gradient adaptation algorithm to find a local maximum of the corresponding approximate likelihood. We present a detailed mathematical derivation of the algorithm using the Lie group invariance. Useful behavior of the algorithm is verified by numerical experiments.
Carlos G. PUNTONET Ali MANSOUR
This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.
HERMANTO Allan Kardec BARROS Tsuyoshi YAMAMURA Noboru OHNISHI
We often see reflection phenomenon in our life. For example, through window glass, we can see real objects, but reflection causes virtual objects to appear in front of the glass. Thus, it is sometimes difficult to recognize the real objects. Some works have been proposed to separate these real and virtual objects using an optical property called polarization. However, they have a restriction on one assumption: the angle of incidence. In this paper, we overcome this difficulty using independent component analysis (ICA). We show the efficiency of the proposed method, by experimental results.
Jianting CAO Noboru MURATA Shun-ichi AMARI Andrzej CICHOCKI Tsunehiro TAKEDA Hiroshi ENDO Nobuyoshi HARADA
Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.
Ali MANSOUR Allan Kardec BARROS Noboru OHNISHI
The blind separation of sources is a recent and important problem in signal processing. Since 1984, it has been studied by many authors whilst many algorithms have been proposed. In this paper, the description of the problem, its assumptions, its currently applications and some algorithms and ideas are discussed.
Hani C. YEHIA Kazuya TAKEDA Fumitada ITAKURA
The objective of this paper is to find a parametric representation for the vocal-tract log-area function that is directly and simply related to basic acoustic characteristics of the human vocal-tract. The importance of this representation is associated with the solution of the articulatory-to-acoustic inverse problem, where a simple mapping from the articulatory space onto the acoustic space can be very useful. The method is as follows: Firstly, given a corpus of log-area functions, a parametric model is derived following a factor analysis technique. After that, the articulatory space, defined by the parametric model, is filled with approximately uniformly distributed points, and the corresponding first three formant frequencies are calculated. These formants define an acoustic space onto which the articulatory space maps. In the next step, an independent component analysis technique is used to determine acoustic and articulatory coordinate systems whose components are as independent as possible. Finally, using singular value decomposition, acoustic and articulatory coordinate systems are rotated so that each of the first three components of the articulatory space has major influence on one, and only one, component of the acoustic space. An example showing how the proposed model can be applied to the solution of the articulatory-to-acoustic inverse problem is given at the end of the paper.