IIM  Vol.2 No.1 , January 2010
Adaptive Parallel Computation for Blind Source Separation with Systolic Architecture
ABSTRACT
The purpose of Blind Source Separation (BSS) is to obtain separated sources from convolutive mixture inputs. Among the various available BSS methods, Independent Component Analysis (ICA) is one of the representative methods. Its key idea is to repetitively update and calculate the measures. However, dealing with the measures obtained from multi-array sensors causes obstacles for real-time use. In order to solve this problem, it is necessary to convert the software implementation of BSS algorithm into the hardware architecture. Through the use of hardware architecture, the BSS algorithm can efficiently work within a relatively short time. In this study, we investigate a practical method using a parallel algorithm and architecture for hardware use in a blind source separation. We design a feedback network for real-time speech signal processing. The network is composed of forward and updates algorithms. The architecture of the network is systolic and therefore it is suitable for parallel processing. We only have to add and connect modules for scaling. This paper covers the process from the systolic design of BSS to the hardware implementation using Xilinx FPGAs. The simulation results of our proposed implementation are also represented in the experimental section. In that section, our architecture returns satisfying results with robust qualities.

Cite this paper
nullH. JEONG, Y. KIM and H. JANG, "Adaptive Parallel Computation for Blind Source Separation with Systolic Architecture," Intelligent Information Management, Vol. 2 No. 1, 2010, pp. 46-52. doi: 10.4236/iim.2010.21006.
References
[1]   T. Lee, A. Bell, and R. Orglmeister, “Blind source separation of real world signals,” in ICNN, 1997.

[2]   C. Jutten and J. Herault, “Blind separation of source, part I: An adaptive algorithm based on neuromimetic architecture,” Signal Processing, Vol. 24, pp. 1–10, 1991.

[3]   F. Asano, S. Ikeda, M. Ogawa, and H. Asoh, and N. Kitawaki, “Combined Approach of Array Processing and Independent Component Analysis for Blind Separation of Acoustic Signals,” IEEE Transactions Speech and Audio Processing, Vol. 11, No. 3, pp. 204–215, 2003.

[4]   S. C. Douglas, Malay Gupta, Hiroshi Sawada, and Shoji Makino, “Spatio-Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures,” IEEE Transactions Audio, Speech and Language Processing, Vol. 15, No. 5, pp. 204–215, 2007.

[5]   R. Aichner, H. Buchner, F. Yan and W. Kellermann, “A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments,” EURASIP Journal on Applied Signal Processing, pp. 1260–1277, 2007.

[6]   M. Ounas, S. Chitroub, R. Touhami, M. C. E. Yagoub, “Digital circuit design of ICA based implementation of FPGA for real time Blind Signal Separation”, MLSP 2008. IEEE Workshop on, October 2008.

[7]   K. Torkkola, “Blind separation of convolved sources based on information maximization,” Proceedings IEEE Workshop Neural Networks for Signal Processing, pp. 423–432, 1996.

[8]   K. Torkkola, “Blind separation of delayed source based on information maximization,” Proceedings ICASSP, Atlanta, GA, pp. 7–10, May 1996.

[9]   K. Torkkola, “Blind Source Separation for Audio Signal-Are we there yet?” IEEE Workshop on Independent Component Analysis and Blind Signal Separation, Aussois, France, January 1999.

[10]   T. Nomura, M. Eguchi, H. Niwamoto, H. Kokubo and M. Miyamoto, “An Extension of The Herault-Jutten Network to Signals Including Delays for Blind Separation,” IEEE Neurals Networks for Signal Processing, VI, pp. 443–452, 1996.

[11]   S. Choi and A. Cichocki, “Adaptive blind separation of speech signals:Cocktail party problem,” in Proeeding International Conference Speech Processing (ICSP’97), pp. 617–622, August 1997.

[12]   N. Charkani and Y. Deville, “Self-adaptive separation of convolutively mixed signals with a recursive structure - part I: Stability analysis and optimization of asymptotic behaviour.” Signal Processing, Vol.73, No. 3, pp. 255– 266, 1999.

 
 
Top