Independent component analysis based on quantum particle swarm optimization

One of the Digital Signal Processing problems is a Blind Source Separation ( BSS ). There are some of methods are employed to solve this problem which are so-called Independent Component Analysis ( ICA ) which based on the statistical distribution of the signal. Many mechanisms are used to improve the ICA as neural networks, genetic algorithm and particle swarm optimization. In this paper, a new method is introduced to improve the performance of the ICA using Quantum Particle Swarm Optimization ( QPSO ). A Negentropy is used as the fitness function of the proposed algorithm to maximize the independence of the statistical distribution of mixed signals, easily separated and recover the original signals. The algorithm is implemented with many speech signals under some conditions as the frequency of 8 kHz, the i.i.d. and well-condition. The proposed method is considered the best from the previous method that depending on some measurements as SNR and SDR . The performance of this method has been tested on two metrics Signal-to-Noise Ratio (SNR) and Signal-to-Distortion Ratio (SDR).