Open Access

On the Vectorization of FIR Filterbanks

EURASIP Journal on Advances in Signal Processing20062007:091741

Received: 20 October 2005

Accepted: 22 June 2006

Published: 29 October 2006


This paper presents a vectorization technique to implement FIR filterbanks. The word vectorization, in the context of this work, refers to a strategy in which all iterative operations are replaced by equivalent vector and matrix operations. This approach allows that the increasing parallelism of the most recent computer processors and systems be properly explored. The vectorization techniques are applied to two kinds of FIR filterbanks (conventional and recursi ve), and are presented in such a way that they can be easily extended to any kind of FIR filterbanks. The vectorization approach is compared to other kinds of implementation that do not explore the parallelism, and also to a previous FIR filter vectorization approach. The tests were performed in Matlab and , in order to explore different aspects of the proposed technique.


Authors’ Affiliations

Department of Communications, FEEC, State University of Campinas (UNICAMP)


  1. Edelman A, McCorquodale P, Toledo S: The future fast Fourier transform? SIAM Journal on Scientific Computing 1999,20(3):1094-1114.MathSciNetView ArticleMATHGoogle Scholar
  2. Frigo M, Johnson SG: FFTW: an adaptive software architecture for the FFT. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 3: 1381-1384.Google Scholar
  3. Thiede TV: Perceptual audio quality assessment using a non-linear filter bank, Ph.D. thesis. Technical University of Berlin, Berlin, Germany; 1999.Google Scholar
  4. Weinhardt M, Luk W: Pipeline vectorization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2001,20(2):234-248. 10.1109/43.908452View ArticleGoogle Scholar
  5. Fahringer T, Scholz B: A unified symbolic evaluation framework for parallelizing compilers. IEEE Transactions on Parallel and Distributed Systems 2000,11(11):1105-1125. 10.1109/71.888633View ArticleGoogle Scholar
  6. Blume W, Eigenmann R, Faigin K, et al.: Polaris: the next generation in parallelizing compilers. Proceedings of the 7th International Workshop in Languages and Compilers for Parallel Computing (LCPC '94), August 1994, Ithaca, NY, USA 10.1-10.18.Google Scholar
  7. Zima H, Chapman B: Supercompilers for Parallel and Vector Computers. Addison-Wesley, New York, NY, USA; 1990.Google Scholar
  8. Silverman HF: A high-quality digital filterbank for speech recognition which runs in real time on a standard microprocessor. IEEE Transactions on Acoustics, Speech, and Signal Processing 1986,34(5):1064-1073. 10.1109/TASSP.1986.1164947View ArticleGoogle Scholar
  9. Redmill DW, Bull DR: Design of low complexity FIR filters using genetic algorithms and directed graphs. Proceedings of the 2nd International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, September 1997, Glasgow, UK 168-173.View ArticleGoogle Scholar
  10. Soderstrand MA, Johnson LG, Arichanthiran H, Hoque MD, Elangovan R: Reducing hardware requirement in FIR filter design. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 6: 3275-3278.Google Scholar
  11. Tan K-H, Leong WF, Kadam S, Soderstrand MA, Johnson LG: Public-domain matlab program to generate highly optimized VHDL for FPGA implementation. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '01), May 2001, Sydney, Australia 514-517.Google Scholar
  12. Brückmann D: Optimized digital signal processing for flexible receivers. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 4: 3764-3767.Google Scholar
  13. Cruz-Roldán F, Monteagudo-Prim M: Efficient implementation of nearly perfect reconstruction FIR cosine-modulated filterbanks. IEEE Transactions on Signal Processing 2004,52(9):2661-2664. 10.1109/TSP.2004.831913View ArticleGoogle Scholar
  14. Sung W, Mitra SK: Implementation of digital filtering algorithms using pipelined vector processors. Proceedings of the IEEE 1987,75(9):1293-1303.View ArticleGoogle Scholar
  15. Meyer MD, Agrawal DP: Vectorization of the DLMS transversal adaptive filter. IEEE Transactions on Signal Processing 1994,42(11):3237-3240. 10.1109/78.330384View ArticleGoogle Scholar
  16. Kim D, Choe G:AMD's 3DNow vectorization for signal processing applications. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 4: 2127-2130.Google Scholar
  17. Robelly JP, Cichon G, Seidel H, Fettweis G: Implementation of recursive digital filters into vector SIMD DSP architectures. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montreal, Canada 5: 165-168.Google Scholar
  18. Van Der Horst M, Van Berkel K, Lukkien J, Mak R: Recursive filtering on a vector DSP with linear speedup. Proceedings of IEEE International Conference on Application-Specific Systems, Architectures and Processors, July 2005, Samos, Greece 379-386.Google Scholar
  19. Shahbahrami A, Juurlink BHH, Vassiliadis S: Efficient vectorization of the FIR filter. Proceedings of the 16th Annual Workshop on Circuits, Systems and Signal Processing (ProRisc '05), November 2005, Veldhoven, The Netherlands 432-437.Google Scholar
  20. Barbedo JGA, Lopes A: A new cognitive model for objective assessment of audio quality. Journal of the Audio Engineering Society 2005,53(1-2):22-31.Google Scholar
  21. Barbedo JGA, Lopes A: A new strategy for objective estimation of the quality of audio signals. IEEE Latin-America Transactions 2004.,2(3):Google Scholar
  22. ITU-R Recommendation BS-1387 : Method for Objective Measurements of Perceived Audio Quality. 1998.Google Scholar
  23. Oppenheim AV, Schafer RW: Discrete-Time Signal Processing. Prentice Hall, Englewood Cliffs, NJ, USA; 1989.MATHGoogle Scholar


© J. G. A. Barbedo and A. Lopes. 2007

This article is published under license to BioMed Central Ltd. 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.