Abstract:
One of the most intractable issues in contemporary digital signal processing, particularly with regards to blind source
separation methods, is known as the cocktail party dilemma. This problem suppose there are many sensors record many signals at same
time to produce many mixed signals. To solve this problem, one of an important methods used for this purpose is an Independent
Component Analysis method. This method abbreviates in how separate mixed signals without any pre-knowledge about the mixing
signals?. It treats on the statistical features of a mixing signals. This work introduces a novel method to solve the cocktail party
problem, by using hybrid method from the Quantum Particle Swarm Optimization method and the Bell- Sejnowski neural method to
enhance the performance of the Independent Component Analysis. In addition, the proposed method uses the Negentropy function to be
the objective function of the optimization process. The proposed algorithm has been implemented on two cases of three really signals,
with 8-KHz frequencies. The results of the separating process measured in two directions: firstly by comparing the results with other
methods as Particle Swarm Optimization and the Quantum Particle Swarm Optimization, where the results appear that the proposed
method appears very high results than other methods. Secondly, by using standard metrics as Absolute Value Correlation Coefficient,
Signal to Distortion Ratio, and Signal to Noise Ratio.
