Digital Signal Processing II Lecture 7: Maximally Decimated Filter Banks Marc Moonen Dept. E.E./ESAT, K.U.Leuven

DSP-II p. 2 Version Lecture-7: Maximally Decimated Filter Banks Part-II : Filter Banks : Preliminaries Filter bank set-up and applications `Perfect reconstruction’ problem + 1st example (DFT/IDFT) Multi-rate systems review (10 slides) : Maximally decimated FBs Perfect reconstruction filter banks (PR FBs) Paraunitary PR FBs : Modulated FBs DFT-modulated FBs Oversampled DFT-modulated FBs : Special Topics Cosine-modulated FBs Non-uniform FBs & Wavelets Frequency domain filtering Lecture-6 Lecture-7 Lecture-8 Lecture-9

DSP-II p. 3 Version Lecture-7: Maximally Decimated Filter Banks PART-II : Filter Banks LECTURE-7 : Maximally decimated FBs Perfect reconstruction (PR) –2-channel case –M-channel case `Interludium’: Paraconjugation & paraunitary functions Paraunitary PR FBs

DSP-II p. 4 Version Lecture-7: Maximally Decimated Filter Banks General `subband processing’ set-up (Lecture-6) : PS: subband processing ignored in filter bank design downsampling/decimation Refresh (1) subband processing 3 H0(z) subband processing 3 H1(z) subband processing 3 H2(z) subband processing 3 H3(z) IN F0(z) F1(z) F2(z) F3(z) + OUT analysis bank synthesis bank upsampling/interpolation

DSP-II p. 5 Version Lecture-7: Maximally Decimated Filter Banks Refresh (2) Two design issues : - filter specifications, e.g. stopband attenuation, passband ripple, transition band, etc. (for each (analysis) filter!) - perfect reconstruction property. PS: Perfect reconstruction property as such is easily satisfied, if there aren’t any (analysis) filter specs, e.g. (see Lecture-6) …but this is not very useful/practical. Tight filter specs. necessary for subband coding, etc. This lecture : Maximally decimated FB’s : u[k-3] u[k]

DSP-II p. 6 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited - analysis filters Hi(z) are also decimation (anti-aliasing) filters, to avoid aliased contributions in subband signals - synthesis filters Gi(z) are also interpolation filters, to remove images after expanders (upsampling) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT

DSP-II p. 7 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (1) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT H1(z)H2(z)H3(z)H4(z) IN

DSP-II p. 8 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (2) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT H1(z)H2(z)H3(z)H4(z) x1

DSP-II p. 9 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (3) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT H1(z)H2(z)H3(z)H4(z) x2

DSP-II p. 10 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (4) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT x’1 (ideal subband processing)

DSP-II p. 11 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (5) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT x’2

DSP-II p. 12 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (6) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT x’’1 (ideal subband processing)

DSP-II p. 13 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (7) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT x’’2

DSP-II p. 14 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (8) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT G1(z)G2(z)G3(z)G4(z) x’’’1 (ideal subband processing)

DSP-II p. 15 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (9) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT G1(z)G2(z)G3(z)G4(z) x’’’2

DSP-II p. 16 Version Lecture-7: Maximally Decimated Filter Banks PS: Filter bank set-up revisited With ideal analysis/synthesis filters, FB operates as follows (10) subband processing 4 H1(z) subband processing 4 H2(z) subband processing 4 H3(z) subband processing 4 H4(z) IN G1(z) G2(z) G3(z) G4(z) + OUT H1(z)H2(z)H3(z)H4(z) OUT=IN Now try this with non-ideal filters…

DSP-II p. 17 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : 2-Channel Case It is proved that... (try it!) U(-z) represents aliased signals, hence the `alias transfer function’ A(z) should ideally be zero T(z) is referred to as `distortion function’ (amplitude & phase distortion). For perfect reconstruction, T(z) should ideally be a pure delay H0(z) H1(z) 2 2 u[k]2 2 F0(z) F1(z) + y[k]

DSP-II p. 18 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : 2-Channel Case Requirement for `alias-free’ filter bank : If A(z)=0, then Y(z)=T(z).U(z), hence the complete filter bank behaves as a linear time invariant (LTI) system (despite up- & downsampling) !!!! Requirement for `perfect reconstruction’ filter bank (= alias-free + distortion-free): i) ii) H0(z) H1(z) 2 2 u[k]2 2 F0(z) F1(z) + y[k]

DSP-II p. 19 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : 2-Channel Case A first attempt is as follows….. : so that For the real coefficient case, i.e. which means the amplitude response of H1 is the mirror image of the amplitude response of Ho with respect to the quadrature frequency hence the name `quadrature mirror filter’ (QMF) H0(z) H1(z) 2 2 u[k]2 2 F0(z) F1(z) + y[k] 

DSP-II p. 20 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : 2-Channel Case `quadrature mirror filter’ (QMF) : hence if Ho (=Fo) is designed to be a good lowpass filter, then H1 (=-F1) is a good high-pass filter. H0(z) H1(z) 2 2 u[k]2 2 F0(z) F1(z) + y[k] HoH1

DSP-II p. 21 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : 2-Channel Case A 2nd (better) attempt is as follows: [Smith & Barnwell 1984] [Mintzer 1985] i) so that (alias cancellation) ii) `power symmetric’ Ho(z) (real coefficients case) iii) so that (distortion function) ignore the details! This is a so-called`paraunitary’ perfect reconstruction bank (see below), based on a lossless system Ho,H1 : This is already pretty complicated…

DSP-II p. 22 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case It is proved that... (try it!) 2nd term represents aliased signals, hence all `alias transfer functions’ Al(z) should ideally be zero (for all l ) H(z) is referred to as `distortion function’ (amplitude & phase distortion). For perfect reconstruction, H(z) should ideally be a pure delay H2(z) H3(z) F2(z) F3(z) y[k] H0(z) H1(z) 4 4 u[k] 4 4 F0(z) F1(z) + Sigh !!…

DSP-II p. 23 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case A simpler analysis results from a polyphase description : i-th row of E(z) has polyphase components of Hi(z) i-th column of R(z) has polyphase components of Fi(z) u[k-3] u[k] Do not continue until you understand how formulae correspond to block scheme!

DSP-II p. 24 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case with the `noble identities’, this is equivalent to: Necessary & sufficient conditions for i) alias cancellation ii) perfect reconstruction are then derived, based on the product u[k-3] u[k]

DSP-II p. 25 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case Necessary & sufficient condition for alias-free FB is…: a pseudo-circulant matrix is a circulant matrix with the additional feature that elements below the main diagonal are multiplied by 1/z, i.e...and first row of R(z).E(z) are polyphase cmpnts of `distortion function’ T(z) read on-> u[k-3] u[k]

DSP-II p. 26 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case PS: This can be explained as follows: first, previous block scheme is equivalent to (cfr. Noble identities) then (iff R.E is pseudo-circ.)… so that finally u[k] T(z)*u[k-3] u[k]

DSP-II p. 27 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case Hence necessary & sufficient condition for PR (where T(z)=pure delay): I_n is nxn identity matrix, r is arbitrary Example (r=0) : for conciseness, will use this from now on ! u[k-3] u[k]

DSP-II p. 28 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case Example : DFT/IDFT Filter bank (Lecture-6) : E(z)=F, R(z)=F^-1 Design Procedure: 1. Design all analysis filters (see Part-I). 2. This determines E(z) (=polyphase matrix). 3. Assuming E(z) can be inverted (?), synthesis filters are Will consider only FIR analysis filters, leading to simple polyphase decompositions (see Lecture-6). However, FIR E(Z) generally leads to IIR R(z), where stability is a concern…

DSP-II p. 29 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case PS: Inversion of matrix transfer functions ?… –The inverse of a scalar (i.e. 1-by-1 matrix) FIR transfer function is always IIR (except for contrived examples) – The inverse of an N-by-N (N>1) FIR transfer function can be FIR

DSP-II p. 30 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case PS: Inversion of matrix transfer functions ?… Compare this to inversion of integers and integer matrices: –The inverse of an integer is always non-integer (except for `E=1’) – The inverse of an N-by-N (N>1) integer matrix can be integer

DSP-II p. 31 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case Question: How can we find polynomial (FIR) matrices E(z) that have a FIR inverse? Answer: `Unimodular’ matrices = matrices with { determinant=constant*z^-d } e.g. where the Ei’s are constant (=not a function of z) invertible matrices Example: E(z) = FIR LPC lattice, see lecture-4 Design Procedure : optimize Ei’s to obtain analysis filter specs (ripple, etc.), etc..

DSP-II p. 32 Version Lecture-7: Maximally Decimated Filter Banks Perfect Reconstruction : M-Channel Case Question: Can we avoid direct inversion, e.g. through the usage FIR E(z) matrices with additional `special properties’, so that R(z) is trivially obtained and its specs are better controlled? (compare with (real) orthogonal or (complex) unitary matrices, where inverse is equal to (Hermitian) transpose) Answer: YES, `paraunitary’ matrices (=special class of FIR matrices with FIR inverse) See next slides…. Will focus on paraunitary E(z) leading to PR/FIR/paraunitary filter banks

DSP-II p. 33 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks Interludium : `PARACONJUGATION’ For a scalar transfer function H(z), paraconjugate is i.e it is obtained from H(z) by - replacing z by 1/z - replacing each coefficient by its complex conjugate Example : On the unit circle, paraconjugation corresponds to complex conjugation paraconjugation = `analytic extension’ of unit-circle complex conjugation

DSP-II p. 34 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks Interludium : `PARACONJUGATION’ For a matrix transfer function H(z), paraconjugate is i.e it is obtained from H(z) by - transposition - replacing z by 1/z - replacing each coefficient by is complex conjugate Example : On the unit circle, paraconjugation corresponds to transpose conjugation paraconjugation = `analytic extension’ of unit-circle transpose conjugation

DSP-II p. 35 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks Interludium : `PARAUNITARY matrix transfer functions’ Matrix transfer function H(z), is paraunitary if (possibly up to a scalar) For a square matrix function A paraunitary matrix is unitary on the unit circle paraunitary = `analytic extension’ of unit-circle unitary. PS: if H1(z) and H2(z) are paraunitary, then H1(z).H2(z) is paraunitary

DSP-II p. 36 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks - If E(z) is paraunitary then perfect reconstruction is obtained with (delta to make synthesis causal) If E(z) is FIR, then R(z) is also FIR !! (cfr. definition paraconjugation) u[k-3] u[k]

DSP-II p. 37 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks Example: paraunitary FIR E(z) with FIR inverse R(z) where the Ei’s are constant unitary matrices Example: 1-input/2-output FIR lossless lattice, see lecture-4, p. 22 Example: 1-input/M-output FIR lossless lattice, see lecture-4, p. 27 Design Procedure : optimize unitary Ei’s (e.g. rotation angles in lossless lattices) to obtain analysis filter specs, etc..

DSP-II p. 38 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks Properties of paraunitary PR filter banks: (proofs omitted) If polyphase matrix E(z) (and hence E(z^N)) is paranunitary, and then vector transfer function H(z) (=all analysis filters ) is paraunitary If vector transfer function H(z) is paraunitary, then its components are power complementary (lossless 1-input/N-output system) (see lecture 4 !!)

DSP-II p. 39 Version Lecture-7: Maximally Decimated Filter Banks Paraunitary PR Filter Banks Properties of paraunitary PR filter banks (continued): Synthesis filters are obtained from analysis filters by conjugating the analysis filter coefficients + reversing the order (cfr page 34): Hence magnitude response of synthesis filter Fk is the same as magnitude response of corresponding analysis filter Hk: Hence, as analysis filters are power complementary (cfr. supra), synthesis filters are also power complementary example: DFT/IDFT bank (lecture-6), 2-channel case (page 21) Great properties/designs....

DSP-II p. 40 Version Lecture-7: Maximally Decimated Filter Banks Conclusions Have derived general conditions for perfect reconstruction, based on polyphase matrices for analysis/synthesis bank Seen example of general PR filter bank design : PR/FIR/Paraunitary FBs, e.g. based on FIR lossless lattice filters Sequel = other (better) PR structures Lecture 7: Modulated filter banks Lecture 8: Oversampled filter banks, etc.. Reference: `Multirate Systems & Filter Banks’, P.P. Vaidyanathan Prentice Hall 1993.