Speech Descrambling Based on Chaotic Parameter Estimation

摘要

The growth of wireless technologies and the use of computer networks have compromised data. Speech scrambling is an essential method of understandable speech elimination and improves information protection in wireless communication applications. The scrambling and descrambling methods have an opposite relation. A chaotic system is a dynamic and deterministic nonlinear system. Its performance is almost random, and states produce chaotic systems dependent on the initial conditions and system parameters and are essential for scrambling speech applications because of their characteristics. This paper suggested using an estimate of the chaotic map parameter, including a logistic map for descrambling speech, and an estimate of the parameter using the meta-heuristic method, including particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO). We suggested minimizing mean square error (MSE) as an objective function. The simulation results show that QPSO is better than PSO for a logistic map's estimated parameter. The signal-to-noise ratio (SNR) was used to measure speech quality.