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International Journal of Reconfigurable Computing
Volume 2018, Article ID 3721756, 9 pages
Research Article

On a Real-Time Blind Signal Separation Noise Reduction System

1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
2School of Electronics and Computer Science, University of Southampton, Malaysia Campus, Iskandar Puteri, Johor, Malaysia

Correspondence should be addressed to Ka Fai Cedric Yiu; kh.ude.uylop@uiycam

Received 30 May 2018; Revised 22 October 2018; Accepted 13 November 2018; Published 4 December 2018

Academic Editor: John Kalomiros

Copyright © 2018 Ka Fai Cedric Yiu and Siow Yong Low. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Blind signal separation has been studied extensively in order to tackle the cocktail party problem. It explores spatial diversity of the received mixtures of sources by different sensors. By using the kurtosis measure, it is possible to select the source of interest out of a number of separated BSS outputs. Further noise cancellation can be achieved by adding an adaptive noise canceller (ANC) as postprocessing. However, the computation is rather intensive and an online implementation of the overall system is not straightforward. This paper intends to fill the gap by developing an FPGA hardware architecture to implement the system. Subband processing is explored and detailed functional operations are profiled carefully. The final proposed FPGA system is able to handle signals with sample rate over 20000 samples per second.