Design of FIR paraunitary filter banks for subband coding using a polynomial eigenvalue decomposition

Soydan Redif, John G. McWhirter, Stephan Weiss

Research output: Contribution to journalArticle

48 Citations (Scopus)
63 Downloads (Pure)

Abstract

The problem of paraunitary filter bank design for subband coding has received considerable attention in recent years, not least because of the energy preserving property of this class of filter banks. In this paper, we consider the design of signal-adapted, finite impulse response (FIR), paraunitary filter banks using polynomial matrix EVD (PEVD) techniques. Modifications are proposed to an iterative, time-domain PEVD method, known as the sequential best rotation (SBR2) algorithm, which enables its effective application to the problem of FIR orthonormal filter bank design for efficient subband coding. By choosing an optimisation scheme that maximises the coding gain at each stage of the algorithm, it is shown that the resulting filter bank behaves more and more like the infiniteorder principle component filter bank (PCFB). The proposed method is compared to state-of-the-art techniques, namely the iterative greedy algorithm (IGA), the approximate EVD (AEVD), standard SBR2 and a fast algorithm for FIR compaction filter design, called the window method (WM). We demonstrate that for the calculation of the subband coder, the WM approach offers a low-cost alternative at lower coding gains, while at moderate to high complexity, the proposed approach outperforms the benchmarkers. In terms of run-time complexity, AEVD performs well at low orders, while the proposed algorithm offers a better coding gain than the benchmarkers at moderate to high filter order for a number of simulation scenarios.
Original languageEnglish
Pages (from-to)5253-5264
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume59
Issue number11
Early online date1 Aug 2011
DOIs
Publication statusPublished - Nov 2011

Keywords

  • algorithm design and analysis
  • compaction
  • covariance matrix
  • encoding
  • matrix decomposition
  • polynomials
  • signal processing algorithms

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