Document Type : Original Research Paper


Electrical & Computer Engineering faculty, University of Tabriz, Iran


In this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. The new method is a hybrid optimization algorithm, which employs the  combination of  the  conventional θ-PSO and the shuffled sub-swarms particle optimization (SSPSO) technique. It is known that the θ-PSO algorithm has better optimization performance than standard PSO algorithm, when dealing with some simple benchmark functions. To improve further the performance of the conventional PSO, the SSPSO algorithm has been suggested to increase the diversity of particles in the swarm. The proposed speech enhancement method, called θ-SSPSO, is a hybrid technique, which incorporates both θ-PSO and SSPSO, with the goal of exploiting the advantages of both algorithms. It is shown that the new θ-SSPSO algorithm is quite effective in achieving global convergence for adaptive filters, which results in a better suppression of noise from input speech signal. Experimental results indicate that the new algorithm outperforms the standard PSO, θ-PSO, and SSPSO in a sense of convergence rate and SNRimprovement.


 [1]      B. Widrow, and S. Stearns, “Adaptive Signal Processing”, Englewood Cliffs, NJ: Prentice Hall, 1985.
[2]      T.Ueda, H suzuki, "Performance of Equilizers Employing a Re-training RLS Algorithm for Digital Mobile Radio Communiations",40th IEEE Vehicular Thechnoly Conference, pp 553-558, 1990.
[3]      Shynk, J.J., “Adaptive IIR Filtering,” IEEE ASSP Magazine, pp. 4-21, April 1989
[4]      Krusicnski, D.J. and Jenkins, W.K.,“Adaptive Filtering Via Particle Swarm Optimization,” Proc. 37’Asilomar Conf on Signals, Systems. And Computers, November 2003.
[5]      D. J. Krusienski and W. K. Jenkins, "Design and Performance of Adaptive Systems Based on Structured Stochastic Optimization Strategies," Circuits and Systems Magazine, Vol. 5, No. 1, pp 8 –20, February 2005.
[6]      P. Mars, J, R. Chen, and R. Nambiar, "Learning Algorithms: Theory and Application in Signal Processing, Control, and Communications", CRCPress, Inc, 1996.
[7]      Hui Wang, Feng Qian, "An Improved Particle Swarm Optimizer with Shuffled Sub_swarm an its Application in Soft-sensor of Gasoline Endpoint",  Atlantis Press, 2007.
[8]      W.M. Zhong, S.J. Li, F. Qian, "θ-PSO: A New Strategy of Particle Swarm Optimization", Journal of Zhejiang University SCIENCE, submitted.
[9]      L.Badri asl ,M geravanchizadeh, "Dual-Channel Speech Enhancement based on Stochastic Optimization Strategies", 10-th International Conference on Information Science, Signal Processing and their applications (ISSPA 2010), Malaysia, 2010.
[10]   J. Kennedy, R.C. Eberhart, Particle swarm optimization. Prof. of the IEEE International Conference on Neural Networks, IEEE Press, pp. 1942–1948, 1995.
[11]   X. Hu, Y.Shi, R.C. Eberhart, “ Recent advances in Particle Swarm,” Prof. of the IEEE International Conference on Evolutionary Computation. Portland, pp.90-97, 2004.
[12]   Felix T.S Chan and Manoj Kumar Tiawari, "Swarm Intelligence Focus on Ant and Particle Swarm Optimization,” First Edition, I-Tech Education and Publishing, December 2007.
[13]   Y Shi, R Eberhart, “Empirical study of particle swarm optimization,” International Conference on Evolutionary Computation, IEEE, Washington, USA, pp. 1945-1950, 1999.
[16]   A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation of speech quality (PESQ)—A new method for speech quality assessment of telephone networks and codecs,” in Proc. 26th IEEE Int. Conf. Acoust. Speech Signal Process., ICASSP-01, vol. 2, pp. 749–752, 2001.
[17]   Philipos C. Loizou, “Speech Enhancement Theory and Practice,” CRC Press, 1 Edition, 2007.
[18]   ITU-R, "Recommendation BS1543-1: Method for the Subjective Assessment of Intermediate Quality Level of Coding Systems", 2001.
[19]   E. Vincent, "MUSHRAM: A MATLAB Interface for MUSHRA Listening Tests", [Online] available.