Speech Scrambling based on Independent Component Analysis and Particle Swarm Optimization

The development of communication technologies and the use of computer networks has led that the data is vulnerable to the violation. For this reason this paper proposed scrambling algorithm based on the Independent Component Analysis (ICA), and the descrambling process was achieved on Particle Swarm Optimization (PSO) to resolve this problem. In the scrambling algorithm, the one speech signals segmented into two types, two and three. It then used the mixing process to result the scrambling of speech. In the descrambling process, we proposed the kurtosis and negative entropies as fitness function. The simulation results indicate that the scrambled speech has no residual intelligibility, and the descrambled speech quality is satisfactory. The performance of scrambling algorithm has been tested on four metrics signal to noise ratio (SNR), Perceptual Evaluation of Speech Quality and Mean Opinion Score (PESQ-MOS), Linear Predictive Coding (LPC) and itakura-saito distance. Many input speech signal of sampling frequency 16 kHz was tested for two genders male and female.