Blind Separation of Nonstationary Sources in Noisy Mixtures

Blind Separation of Nonstationary Sour es in Noisy Mixtures

Blind Separation of Nonstationary Sources by Spectral Decorrelation

This paper demonstrates and exploits some interesting frequency-domain properties of nonstationary signals. Considering these properties, two new methods for blind separation of linear instantaneous mixtures of mutually uncorrelated, nonstationary sources are proposed. These methods are based on spectral decorrelation of the sources. The second method is particularly important because it allows...

From Time-domain Separation of Stationary Temporally Correlated Sources to Frequency-domain Separation of Nonstationary Sources

This paper demonstrates and exploits some interesting frequency-domain properties of nonstationary signals. Considering these properties, a new method for blind separation of linear instantaneous mixtures of mutually uncorrelated, nonstationary, real sources is proposed which is based on spectral decorrelation of the sources. It allows the existing timedomain algorithms developed for stationary...

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...

Research of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...