Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component From Nonstationary Mixtures — Algorithms

Blind separation of instantaneous mixtures of nonstationary sources

Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible) non-stationarity of the sources to achieve separation. We introduce two objective functions based on the likelihood and on mutual information in a simple Gaussian non stationary model and we show how they can be optimized, off-line or on-line, by simple yet remarkably efficient algorit...

Blind Separation of Nonstationary Sources in Noisy Mixtures

In this letter we develop a new method of blind source separation that is not sensitive to additive white noise. Our method exploits the nonstationarity and temporal structure of sources. The method needs only multiple time-delayed correlation matrices of the observed data at several di erent time-windowed data frames to estimate the mixing matrix. The implementation using joint diagonalization...

Blind Separation of Nonstationary Sour es in Noisy Mixtures

Nonstationary Matrix Cascade Algorithms

This paper gives results on weak and strong convergence in L 2 (IR s) r of the cascade sequence (k;n) generated by the nonstation-ary matrix cascade algorithm k;n = jMj P j h k+1 (j) k+1;n?1 (M ?j); where for each k = 1; 2; : : : ; h k is a nite sequence of r r matrices and M is an integer dilation matrix. The limit as n ! 1 of the cascade sequence is an r-vector of functions (k) that is a solu...

Blind Separation of Nonstationary and Temporally Correlated Sources from Noisy Mixtures

In this paper we present a new method of blind source separation that is robust with respect to additive white noise. Our method exploits the nonstationarity and temporal structure of sources. The method needs only multiple time-delayed correlation matrices of the observation data at several di erent time-windowed frames to estimate the mixing matrix. We present an implementation based on the j...