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This paper introduces the application of particle Swarm optimization techniques in speech enhancement structures. Because of the simple conception, the quick velocity of convergence and easy implementation, Particle swarm optimization (PSO) is used as an effective method in a wide variety of engineering problems. An improved PSO algorithm, called the Modified PSO (MPSO) algorithm, can find optimal or closer-to optimal solution, and has faster search speed. In this paper, we use PSO and MPSO algorithms for speech enhancement and compare the results. Experimental results show that MPSO algorithm outperforms the PSO algorithm in a sense of SNR improvement.

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This paper proposes a novel particle swarm optimization algorithm based on sexual reproduction for speech enhancement. In the proposed algorithm, the population is divided in two equal subgroups: male and female. The male subgroup pays more attention to the global search, but the female subgroup focuses on the local search. Therefore, females utilize local search capability in prosperous regions, while males search the problem space globally to avoid en trapment in a local minimum. The crossover operation between two groups adds new solutions to the population. Experimental results indicate that the sexual reproduction-based particle swarm optimization (SRPSO) outpeiforms the standard particle swarm optimization (SPSO), and gradient-based NLMS algorithm in speech enhancement application.