Low delay modulated filter bank

Abstract

The document relates to modulated digital filter banks, as well as to methods and systems for the design of such filter banks. In particular, the document discloses a method comprising accessing a time-domain audio signal and applying a first filter bank to the time-domain audio signal, thereby producing a first plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal. The filter bank comprises an decimated modulated filter bank obtained from an asymmetric prototype filter. The method further comprises applying a second filter bank to at least a first subband of the first plurality of subbands of frequency-domain audio data, thereby producing a second plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal. The second modulated filter bank comprises an asymmetric modulated filter bank which includes no decimation. The method further comprises outputting at least the second plurality of subbands of frequency domain audio data.

Claims

1. An apparatus comprising: an interface configured to receive time-domain audio signals and to output frequency-domain audio data; and a logic system in communication with the interface, the logic system being configured to: apply a first filter bank to a time-domain audio signal, thereby producing a first plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal, wherein the filter bank comprises an decimated modulated filter bank obtained from an asymmetric prototype filter; apply a second filter bank to at least a first subband of the first plurality of subbands of frequency-domain audio data, thereby producing a second plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal, wherein the second modulated filter bank comprises an asymmetric modulated filter bank which includes no decimation; and output, via the interface, at least the second plurality of subbands of frequency domain audio data.

2. The apparatus of claim 1 wherein the asymmetric modulated filter bank which includes no decimation is a Nyquist filter bank.

3. The apparatus of claim 2 wherein the Nyquist filter bank is obtained using a Nyquist prototype filter of length eleven.

4. The apparatus of claim 2 wherein the Nyquist filter bank is obtained using a Nyquist prototype filter with a delay of three samples.

5. The apparatus of claim 1 wherein said first subband is the lowest-frequency subband of the first plurality of subbands of frequency-domain audio data.

6. The apparatus of claim 1 wherein the logic system is configured to apply the second filter bank to only said first subband, and wherein the second plurality of subbands of frequency-domain audio data consists of eight subbands of frequency-domain audio data.

7. The apparatus of claim 1 wherein the logic system is further configured to: apply a third filter bank to at least a second subband of the first plurality of subbands of frequency-domain audio data, thereby producing a third plurality of subbands of frequency-domain audio data representative of a part of the audio signal, wherein the third modulated filter bank comprises an asymmetric modulated filter bank which includes no decimation output, via the interface, the third plurality of subbands of frequency domain audio data.

8. The apparatus of claim 1 wherein the asymmetric modulated filter bank which includes no decimation is a Nyquist filter bank.

9. The apparatus of claim 8 wherein the Nyquist filter bank is obtained using a Nyquist prototype filter of length eleven.

10. The apparatus of claim 8 wherein the Nyquist filter bank is obtained using a Nyquist prototype filter with a delay of three samples.

11. The apparatus of claim 7 wherein said second subband is the second-lowest-frequency subband of the first plurality of subbands of frequency-domain audio data.

12. The apparatus of claim 7 wherein the logic system is configured to apply the third filter bank to only said second subband, and wherein the third plurality of subbands of frequency-domain audio data consists of four subbands of frequency-domain audio data.

13. The apparatus of claim 1 wherein the logic system is further configured to: apply a fourth filter bank to at least a third subband of the first plurality of subbands of frequency-domain audio data, thereby producing a fourth plurality of subbands of frequency-domain audio data representative of a part of the audio signal, wherein the fourth modulated filter bank comprises an asymmetric modulated filter bank which includes no decimation output, via the interface, the fourth plurality of subbands of frequency domain audio data.

14. The apparatus of claim 13 wherein the asymmetric modulated filter bank which includes no decimation is a Nyquist filter bank.

15. The apparatus of claim 14 wherein the Nyquist filter bank is obtained using a Nyquist prototype filter of length eleven.

16. The apparatus of claim 14 wherein the Nyquist filter bank is obtained using a Nyquist prototype filter with a delay of three samples.

17. The apparatus of claim 13 wherein said third subband is the third-lowest-frequency subband of the first plurality of subbands of frequency-domain audio data.

18. The apparatus of claim 13 wherein the logic system is configured to apply the fourth filter bank to only said third subband, and wherein the fourth plurality of subbands of frequency-domain audio data consists of four subbands of frequency-domain audio data.

19. A method comprising: accessing a time-domain audio signal; applying a first filter bank to the time-domain audio signal, thereby producing a first plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal, wherein the filter bank comprises an decimated modulated filter bank obtained from an asymmetric prototype filter; applying a second filter bank to at least a first subband of the first plurality of subbands of frequency-domain audio data, thereby producing a second plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal, wherein the second modulated filter bank comprises an asymmetric modulated filter bank which includes no decimation; and outputting at least the second plurality of subbands of frequency domain audio data.

20. A non-transitory, computer-readable storage medium on which is stored a computer program which, when executed by a logic system of an apparatus which comprises the logic system and, in communication therewith, an interface configured to receive time-domain audio signals and to output frequency-domain audio data, configured the logic system to: apply a first filter bank to a time-domain audio signal, thereby producing a first plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal, wherein the filter bank comprises an decimated modulated filter bank obtained from an asymmetric prototype filter; apply a second filter bank to at least a first subband of the first plurality of subbands of frequency-domain audio data, thereby producing a second plurality of subbands of frequency-domain audio data representative of at least a part of the audio signal, wherein the second modulated filter bank comprises an asymmetric modulated filter bank which includes no decimation; and output, via the interface, at least the second plurality of subbands of frequency domain audio data.

Description

(1) The present invention will now be described by way of illustrative examples, not limiting the scope, with reference to the accompanying drawings, in which:

(2) FIG. 1 illustrates the analysis and synthesis sections of a digital filter bank;

(3) FIG. 2 shows the stylized frequency responses for a set of filters to illustrate the adverse effect when modifying the subband samples in a cosine modulated, i.e. real-valued, filter bank;

(4) FIG. 3 shows a flow diagram of an example of the optimization procedure;

(5) FIG. 4 shows a time domain plot and the frequency response of an optimized prototype filter for a low delay modulated filter bank having 64 channels and a total system delay of 319 samples;

(6) FIG. 5 illustrates an example of the analysis and synthesis parts of a low delay complex-exponential modulated filter bank system;

(7) FIG. 6 illustrates an example of an audio signal processing chain comprising analysis, processing and synthesis sections;

(8) FIG. 7 illustrates in more detail the analysis section illustrated in FIG. 6; and

(9) FIG. 8 shows a flow diagram of an example procedure for designing a prototype filter for use in the secondary filter bank section of the analysis section, illustrated in FIG. 7.

(10) It should be understood that the present teachings are applicable to a range of implementations that incorporate digital filter banks other than those explicitly mentioned in this patent. In particular, the present teachings may be applicable to other methods for designing a filter bank on the basis of a prototype filter.

(11) In the following, the overall transfer function of an analysis/synthesis filter bank is determined. In other words, the mathematical representation of a signal passing through such a filter bank system is described. A digital filter bank is a collection of M, M being two or more, parallel digital filters that share a common input or a common output. For details on such filter banks, reference is made to Multirate Systems and Filter Banks P. P. Vaidyanathan Prentice Hall: Englewood Cliffs, N.J., 1993. When sharing a common input the filter bank may be called an analysis bank. The analysis bank splits the incoming signal into M separate signals called subband signals. The analysis filters are denoted H.sub.k(z), where k=0, . . . , M1. The filter bank is critically sampled or maximally decimated when the subband signals are decimated by a factor M. Thus, the total number of subband samples per time unit across all subbands is the same as the number of samples per time unit for the input signal. The synthesis bank combines these subband signals into a common output signal. The synthesis filters are denoted F.sub.k(z), for k=0, . . . , M1.

(12) A maximally decimated filter bank with M channels or subbands is shown in FIG. 1. The analysis part 101 produces from the input signal X(z) the subband signals V.sub.k(z), which constitute the signals to be transmitted, stored or modified. The synthesis part 102 recombines the signals V.sub.k(z) to the output signal {circumflex over (X)}(z).

(13) The recombination of V.sub.k(z) to obtain the approximation {circumflex over (X)}(z) of the original signal X(z) is subject to several potential errors. The errors may be due to an approximation of the perfect reconstruction property, and includes non-linear impairments due to aliasing, which may be caused by the decimation and interpolation of the subbands. Other errors resulting from approximations of the perfect reconstruction property may be due to linear impairments such as phase and amplitude distortion.

(14) Following the notations of FIG. 1, the outputs of the analysis filters H.sub.k(z) 103 are
X.sub.k(z)=H.sub.k(z)X(z), (1)

(15) where k=0, . . . , M1. The decimators 104, also referred to as down-sampling units, give the outputs

(16) $\begin{array}{cc}{V}_k\left(z\right)=\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}{X}_k\left(z^{1/M}{W}^l\right)=\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}{H}_k\left(z^{1/M}{W}^l\right)X\left(z^{1/M}{W}^l\right),& \left(2\right)\end{array}$

(17) where W=e.sup.i2/M. The outputs of the interpolators 105, also referred to as up-sampling units, are given by

(18) 0$\begin{array}{cc}{U}_k\left(z\right)=V_k\left(z^M\right)=\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}{H}_k\left({\mathrm{zW}}^l\right)X\left({\mathrm{zW}}^l\right),& \left(3\right)\end{array}$

(19) and the sum of the signals obtained from the synthesis filters 106 can be written as

(20) $\begin{array}{cc}\begin{array}{c}\hat{X}\left(z\right)=\underset{k=0}{\overset{M-1}{.Math.}}{F}_k\left(z\right)U_k\left(z\right)=\underset{k=0}{\overset{M-1}{.Math.}}{F}_k\left(z\right)\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}{H}_k\left({\mathrm{zW}}^l\right)X\left({\mathrm{zW}}^l\right)=\\ =\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}X\left({\mathrm{zW}}^l\right)\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left({\mathrm{zW}}^l\right)F_k\left(z\right)\\ =\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}X\left({\mathrm{zW}}^l\right)A_l\left(z\right)\end{array}& \left(4\right)\\ \mathrm{where}& \\ A_l\left(z\right)=\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left({\mathrm{zW}}^l\right)F_k\left(z\right)& \left(5\right)\end{array}$

(21) is the gain for the l.sup.th alias term X(zW.sup.l). Eq. (4) shows that {circumflex over (X)}(z) is a sum of M components consisting of the product of the modulated input signal X(zW.sup.l) and the corresponding alias gain term A.sub.l(z). Eq. (4) can be rewritten as

(22) $\begin{array}{cc}\hat{X}\left(z\right)=\frac{1}{M}\left\{X\left(z\right)A_0\left(z\right)+\underset{l=1}{\overset{M-1}{.Math.}}X\left({\mathrm{zW}}^l\right)A_l\left(z\right)\right\}.& \left(6\right)\end{array}$

(23) The last sum on the right hand side (RHS) constitutes the sum of all non-wanted alias terms. Canceling all aliasing, that is forcing this sum to zero by means of proper choices of H.sub.k(z) and F.sub.k(z), gives

(24) $\begin{array}{cc}\hat{X}\left(z\right)=\frac{1}{M}X\left(z\right)A_0\left(z\right)=\frac{1}{M}X\left(z\right)\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left(z\right)F_k\left(z\right)=X\left(z\right)T\left(z\right),\mathrm{where}& \left(7\right)\\ T\left(z\right)=\frac{1}{M}\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left(z\right)F_k\left(z\right)& \left(8\right)\end{array}$

(25) is the overall transfer function or distortion function. Eq. (8) shows that, depending on H.sub.k(z) and F.sub.k(z), T(z) could be free from both phase distortion and amplitude distortion. The overall transfer function would in this case simply be a delay of D samples with a constant scale factor c, i.e.
T(z)=cz.sup.D, (9)

(26) which substituted into Eq. (7) gives
{circumflex over (X)}(z)=cz.sup.DX(z). (10)

(27) The type of filters that satisfy Eq. (10) are said to have the perfect reconstruction (PR) property. If Eq. (10) is not perfectly satisfied, albeit satisfied approximately, the filters are of the class of approximate perfect reconstruction filters.

(28) In the following, a method for designing analysis and synthesis filter banks from a prototype filter is described. The resulting filter banks are referred to as cosine modulated filter banks. In the traditional theory for cosine modulated filter banks, the analysis filters h.sub.k(n) and synthesis filters f.sub.k(n) are cosine modulated versions of a symmetric low-pass prototype filter p.sub.0(n), i.e.

(29) $\begin{array}{cc}{h}_k\left(n\right)=\sqrt{2}{p}_0\left(n\right)\cos \left\{\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{N}{2}\frac{M}{2}\right)\right\},0nN,0k<M& \left(11\right)\\ f_k\left(n\right)=\sqrt{2}{p}_0\left(n\right)\cos \left\{\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{N}{2}\frac{M}{2}\right)\right\},0nN,0k<M& \left(12\right)\end{array}$

(30) respectively, where M is the number of channels of the filter bank and N is the prototype filter order.

(31) The above cosine modulated analysis filter bank produces real-valued subband samples for real-valued input signals. The subband samples are down sampled by a factor M, making the system critically sampled. Depending on the choice of the prototype filter, the filter bank may constitute an approximate perfect reconstruction system, i.e. a so called pseudo QMF bank described e.g. in U.S. Pat. No. 5,436,940, or a perfect reconstruction (PR) system. An example of a PR system is the modulated lapped transform (MLT) described in further detail in Lapped Transforms for Efficient Transform/Subband Coding H. S. Malvar, IEEE Trans ASSP, vol. 38, no. 6, 1990. The overall delay, or system delay, for a traditional cosine modulated filter bank is N.

(32) In order to obtain filter bank systems having lower system delays, the present document teaches to replace the symmetric prototype filters used in conventional filter banks by asymmetric prototype filters. In the prior art, the design of asymmetric prototype filters has been restricted to systems having the perfect reconstruction (PR) property. Such a perfect reconstruction system using asymmetric prototype filters is described in EP0874458. However, the perfect reconstruction constraint imposes limitations to a filter bank used in e.g. an equalization system, due to the restricted degrees of freedom when designing the prototype filter. It should by noted that symmetric prototype filters have a linear phase, i.e. they have a constant group delay across all frequencies. On the other hand, asymmetric filters typically have a non-linear phase, i.e. they have a group delay which may change with frequency.

(33) In filter bank systems using asymmetric prototype filters, the analysis and synthesis filters may be written as

(34) $\begin{array}{cc}{h}_k\left(n\right)=\sqrt{2}{\hat{h}}_0\left(n\right)\cos \left\{\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}\frac{M}{2}\right)\right\},0n<N_h,0k<M& \left(13\right)\\ f_k\left(n\right)=\sqrt{2}{\hat{f}}_0\left(n\right)\cos \left\{\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}\frac{M}{2}\right)\right\},0n<N_f,0k<M& \left(14\right)\end{array}$

(35) respectively, where .sub.0(n) and {circumflex over (f)}.sub.0(n) are the analysis and synthesis prototype filters of lengths N.sub.h and N.sub.f, respectively, and D is the total delay of the filter bank system. Without limiting the scope, the modulated filter banks studied in the following are systems where the analysis and synthesis prototypes are identical, i.e.
{circumflex over (f)}.sub.0(n)=.sub.0(n)=p.sub.0(n), 0n<N.sub.h=N.sub.f=N (15)

(36) where N is the length of the prototype filter p.sub.0(n).

(37) It should be noted, however, when using the filter design schemes outlined in the present document, that filter banks using different analysis and synthesis prototype filters may be determined.

(38) One inherent property of the cosine modulation is that every filter has two pass bands; one in the positive frequency range and one corresponding pass band in the negative frequency range. It can be verified that the so-called main, or significant, alias terms emerge from overlap in frequency between either the filters negative pass bands with frequency modulated versions of the positive pass bands, or reciprocally, the filters positive pass bands with frequency modulated versions of the negative pass bands. The last terms in Eq. (13) and (14), i.e. the terms

(39) $\frac{}{2}\left(k+\frac{1}{2}\right),$
are selected so as to provide cancellation of the main alias terms in cosine modulated filter banks. Nevertheless, when modifying the subband samples, the cancellation of the main alias terms is impaired, thereby resulting in a strong impact of aliasing from the main alias terms. It is therefore desirable to remove these main alias terms from the subband samples altogether.

(40) The removal of the main alias terms may be achieved by the use of so-called Complex-Exponential Modulated Filter Banks which are based on an extension of the cosine modulation to complex-exponential modulation. Such extension yields the analysis filters h.sub.k(n) as

(41) $\begin{array}{cc}{h}_k\left(n\right)=p_0\left(n\right)\exp \left\{i\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}\frac{M}{2}\right)\right\},0n<N,0k<M& \left(16\right)\end{array}$

(42) using the same notation as before. This can be viewed as adding an imaginary part to the real-valued filter bank, where the imaginary part consists of sine modulated versions of the same prototype filter. Considering a real-valued input signal, the output from the filter bank can be interpreted as a set of subband signals, where the real and the imaginary parts are Hilbert transforms of each other.

(43) The resulting subbands are thus the analytic signals of the real-valued output obtained from the cosine modulated filter bank. Hence, due to the complex-valued representation, the subband signals are over-sampled by a factor two.

(44) The synthesis filters are extended in the same way to

(45) $\begin{array}{cc}{f}_k\left(n\right)=p_0\left(n\right)\exp \left\{i\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}\frac{M}{2}\right)\right\},0n<N,0k<M.& \left(17\right)\end{array}$

(46) Eq. (16) and (17) imply that the output from the synthesis bank is complex-valued. Using matrix notation, where C.sub.a is a matrix with the cosine modulated analysis filters from Eq. (13), and S.sub.a is a matrix with the sine modulation of the same argument, the filters of Eq. (16) are obtained as C.sub.a+j S.sub.a. In these matrices, k is the row index and n is the column index. Analogously, the matrix C.sub.s has synthesis filters from Eq. (14), and S.sub.s is the corresponding sine modulated version. Eq. (17) can thus be written C.sub.s+j S.sub.s, where k is the column index and n is the row index. Denoting the input signal x, the output signal y is found from
y=(C.sub.s+j S.sub.s)(C.sub.a+j S.sub.a)x=(C.sub.sC.sub.aS.sub.sS.sub.a)x+j(C.sub.sS.sub.a+S.sub.sC.sub.a) x (18)

(47) As seen from Eq. (18), the real part comprises two terms; the output from the cosine modulated filter bank and an output from a sine modulated filter bank. It is easily verified that if a cosine modulated filter bank has the PR property, then its sine modulated version, with a change of sign, constitutes a PR system as well. Thus, by taking the real part of the output, the complex-exponential modulated system offers the same reconstruction accuracy as the corresponding cosine modulated version. In other words, when using a real-valued input signal, the output signal of the complex-exponential modulated system may be determined by taking the real part of the output signal.

(48) The complex-exponential modulated system may be extended to handle also complex-valued input signals. By extending the number of channels to 2M, i.e. by adding the filters for negative frequencies, and by keeping the imaginary part of the output signal, a pseudo QMF or a PR system for complex-valued signals is obtained.

(49) It should be noted that the complex-exponential modulated filter bank has one pass band only for every filter in the positive frequency range. Hence, it is free from the main alias terms. The absence of main alias terms makes the aliasing cancellation constraint from the cosine (or sine) modulated filter bank obsolete in the complex-exponential modulated version. The analysis and synthesis filters can thus be given as

(50) $\begin{array}{cc}{h}_k\left(n\right)=p_0\left(n\right)\exp \left\{i\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}-A\right)\right\},0n<N,0k<M\mathrm{and}& \left(19\right)\\ f_k\left(n\right)=p_0\left(n\right)\exp \left\{i\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}+A\right)\right\},0n<N,0k<M& \left(20\right)\end{array}$

(51) where A is an arbitrary (possibly zero) constant, and as before, M is the number of channels, N is the prototype filter length, and D is the system delay. By using different values of A, more efficient implementations of the analysis and synthesis filter banks, i.e. implementations with reduced complexity, can be obtained.

(52) Before presenting a method for optimization of prototype filters, the disclosed approaches to the design of filter banks are summarized. Based on symmetric or asymmetric prototype filters, filter banks may be generated e.g. by modulating the prototype filters using a cosine function or a complex-exponential function. The prototype filters for the analysis and synthesis filter banks may either be different or identical. When using complex-exponential modulation, the main alias terms of the filter banks are obsolete and may be removed, thereby reducing the aliasing sensitivity to modifications of the subband signals of the resulting filter banks. Furthermore, when using asymmetric prototype filters the overall system delay of the filter banks may be reduced. It has also been shown that when using complex-exponential modulated filter banks, the output signal from a real valued input signal may be determined by taking the real part of the complex output signal of the filter bank.

(53) In the following a method for optimization of the prototype filters is described in detail. Depending on the needs, the optimization may be directed at increasing the degree of perfect reconstruction, i.e. at reducing the combination of aliasing and amplitude distortion, at reducing the sensitivity to aliasing, at reducing the system delay, at reducing phase distortion, and/or at reducing amplitude distortion. In order to optimize the prototype filter p.sub.0(n) first expressions for the alias gain terms are determined. In the following, the alias gain terms for a complex exponential modulated filter bank are derived. However, it should be noted that the alias gain terms outlined are also valid for a cosine modulated (real valued) filter bank.

(54) Referring to Eq. (4), the z-transform of the real part of the output signal {circumflex over (x)}(n) is

(55) 0$\begin{array}{cc}Z\left\{\mathrm{Re}\left(\hat{x}\left(n\right)\right)\right\}={\hat{X}}_R\left(z\right)=\frac{\hat{X}\left(z\right)+\hat{X}*\left(z\right)}{2}.& \left(21\right)\end{array}$

(56) The notation {circumflex over (X)}*(z) is the z-transform of the complex-conjugated sequence {circumflex over (x)}(n). From Eq. (4), it follows that the transform of the real part of the output signal is

(57) $\begin{array}{cc}{\hat{X}}_R\left(z\right)=\frac{1}{M}\underset{l=0}{\overset{M-1}{.Math.}}\frac{1}{2}\left(X\left({\mathrm{zW}}^l\right)A_l\left(z\right)+X\left({\mathrm{zW}}^{-l}\right)A_l*\left(z\right)\right),& \left(22\right)\end{array}$

(58) where it was used that the input signal x(n) is real-valued, i.e. X*(zW.sup.l)=X(zW.sup.l). Eq. (22) may after rearrangement be written

(59) $\begin{array}{cc}\begin{array}{c}{\hat{X}}_R\left(z\right)=\frac{1}{M}(X\left(z\right)\frac{1}{2}\left(A_0\left(z\right)+A_0*\left(z\right)\right)+\\ \underset{l=1}{\overset{M-1}{.Math.}}\frac{1}{2}\left(X\left({\mathrm{zW}}^l\right)A_l\left(z\right)+X\left({\mathrm{zW}}^{M-l}\right)A_l*\left(z\right)\right))=\\ =\frac{1}{M}(X\left(z\right)\frac{1}{2}\left(A_0\left(z\right)+A_0*\left(z\right)\right)+\\ \underset{l=1}{\overset{M-1}{.Math.}}X\left({\mathrm{zW}}^l\right)\frac{1}{2}\left(A_l\left(z\right)+A_{M-l}*\left(z\right)\right))=\\ =\frac{1}{M}\left(X\left(z\right){\tilde{A}}_0\left(z\right)+\underset{l=1}{\overset{M-1}{.Math.}}Z\left({\mathrm{zW}}^l\right){\tilde{A}}_l\left(z\right)\right)\end{array}& \left(23\right)\end{array}$

(60) where
.sub.l(z)=(A.sub.l(z)+A.sub.Ml*(z)), 0l<M (24)

(61) are the alias gain terms used in the optimization. It can be observed from Eq. (24) that
.sub.Ml(z)=(A.sub.Ml(z)+A.sub.l*(z))=.sub.l*(z). (25)

(62) Specifically, for real-valued systems
A.sub.Ml*(z)=A.sub.l(z) (26)

(63) which simplifies Eq. (24) into
.sub.l(z)=A.sub.l(z), 0l<M. (27)

(64) By inspecting Eq. (23), and recalling the transform of Eq. (21), it can be seen that the real part of a.sub.0(n) must be a Dirac pulse for a PR system, i.e. .sub.0(z) is on the form .sub.0(z)=c z.sup.D. Moreover, the real part of a.sub.M/2(n) must be zero, i.e. .sub.M/2(z) must be zero, and the alias gains, for l0, M/2 must satisfy
A.sub.Ml(z)=A.sub.l*(z), (28)

(65) which for a real-valued system, with Eq. (26) in mind, means that all a.sub.l(n), l=1 . . . M1 must be zero. In pseudo QMF systems, Eq. (28) holds true only approximately. Moreover, the real part of a.sub.0(n) is not exactly a Dirac-pulse, nor is the real part of a.sub.M/2(n) exactly zero.

(66) Before going into further details on the optimization of the prototype filters, the impact of modifications of the subband samples on aliasing is investigated. As already mentioned above, changing the gains of the channels in a cosine modulated filter bank, i.e. using the analysis/synthesis system as an equalizer, renders severe distortion due to the main alias terms. In theory, the main alias terms cancel each other out in a pair wise fashion. However, this theory of main alias term cancellation breaks, when different gains are applied to different subband channels. Hence, the aliasing in the output signal may be substantial. To show this, consider a filter bank where channel p and higher channels are set to zero gain, i.e.

(67) $\begin{array}{cc}\begin{array}{cc}{v}_k^{}\left(n\right)=g_k{v}_k\left(n\right),& \{\begin{array}{cc}{g}_k=1,& 0k<p\\ g_k=0,& pk<M-1\end{array}\end{array}& \left(29\right)\end{array}$

(68) The stylized frequency responses of the analysis and synthesis filters of interest are shown in FIG. 2. FIG. 2(a) shows the synthesis channel filters F.sub.p1(z) and F.sub.p(z), highlighted by reference signs 201 and 202, respectively. As already indicated above, the cosine modulation for each channel results in one positive frequency filter and one negative frequency filter. In other words, the positive frequency filters 201 and 202 have corresponding negative frequency filters 203 and 204, respectively.

(69) The p.sup.th modulation of the analysis filter H.sub.p1(z), i.e. H.sub.p1(zW.sup.p) indicated by reference signs 211 and 213, is depicted in FIG. 2(b) together with the synthesis filter F.sub.p1(z), indicated by reference signs 201 and 203. In this Figure, reference sign 211 indicates the modulated version of the originally positive frequency filter H.sub.p1(z) and reference sign 213 indicates the modulated version of the originally negative frequency filter H.sub.p1(z). Due to the modulation of order p, the negative frequency filter 213 is moved to the positive frequency area and therefore overlaps with the positive synthesis filter 201. The shaded overlap 220 of the filters illustrates the energy of a main alias term.

(70) In FIG. 2(c) the p.sup.th modulation of H.sub.p(z), i.e. H.sub.p(zW.sup.p) indicated by reference signs 212 and 214, is shown together with the corresponding synthesis filter F.sub.p(z), reference signs 202 and 204. Again the negative frequency filter 214 is moved into the positive frequency area due to the modulation of order p. The shaded area 221 again pictorially shows the energy of a main alias term and would un-cancelled typically result in significant aliasing. To cancel the aliasing, the term should be the polarity reversed copy of the aliasing obtained from the intersection of filters H.sub.p1(zW.sup.p), 213, and F.sub.p1(z), 201, of FIG. 2(b), i.e. the polarity reversed copy of the shaded area 220. In a cosine modulated filter bank, where the gains are unchanged, these main alias terms will usually cancel each other completely. However, in this example, the gain of the analysis (or synthesis) filter p is zero, so the aliasing induced by filters p1 will remain un-cancelled in the output signal. An equally strong aliasing residue will also emerge in the negative frequency range.

(71) When using complex-exponential modulated filter banks, the complex-valued modulation results in positive frequency filters only. Consequently, the main alias terms are gone, i.e. there is no significant overlap between the modulated analysis filters H.sub.p(zW) and their corresponding synthesis filters F.sub.p(z) and aliasing can be reduced significantly when using such filter bank systems as equalizers. The resulting aliasing is dependent only on the degree of suppression of the remaining alias terms.

(72) Hence, even when using complex-exponential modulated filter banks, it is crucial to design a prototype filter for maximum suppression of the alias gains terms, although the main alias terms have been removed for such filter banks. Even though the remaining alias terms are less significant than the main alias terms, they may still generate aliasing which causes artifacts to the processed signal. Therefore, the design of such a prototype filter can preferably be accomplished by minimizing a composite objective function. For this purpose, various optimization algorithms may be used. Examples are e.g. linear programming methods, Downhill Simplex Method or a non-constrained gradient based method or other nonlinear optimization algorithms. In an exemplary embodiment an initial solution of the prototype filter is selected. Using the composite objective function, a direction for modifying the prototype filter coefficients is determined which provides the highest gradient of the composite objective function. Then the filter coefficients are modified using a certain step length and the iterative procedure is repeated until a minimum of the composite objective function is obtained. For further details on such optimization algorithms, reference is made to Numerical Recipes in C, The Art of Scientific Computing, Second Edition W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Cambridge University Press, NY, 1992, which is incorporated by reference.

(73) For improved alias term minimization (IATM) of the prototype filter, a preferred objective function may be denoted
e.sub.tot()=e.sub.t+(1)e.sub.a, (30)

(74) where the total error e.sub.tot() is a weighted sum of the transfer function error e.sub.t and the aliasing error e.sub.a. The first term on the right hand side (RHS) of Eq. (23) evaluated on the unit circle, i.e. for z=e.sup.j, can be used to provide a measure of the error energy e.sub.t of the transfer function as

(75) $\begin{array}{cc}{e}_t=\frac{1}{2}\stackrel{}{\underset{-}{}}{.Math.\frac{1}{2}\left(A_0\left(e^j\right)+A_0^{*}\left(e^{-j}\right)\right)-P\left(\right)e^{-jD}.Math.}^{2}d,& \left(31\right)\end{array}$

(76) where P() is a symmetric real-valued function defining the pass band and stop band ranges, and D is the total system delay. In other words, P() describes the desired transfer function. In the most general case, such transfer function comprises a magnitude which is a function of the frequency . For a real-valued system Eq. (31) simplifies to

(77) $\begin{array}{cc}{e}_t=\frac{1}{2}\stackrel{}{\underset{-}{}}{.Math.A_0\left(e^j\right)-P\left(\right)e^{-jD}.Math.}^{2}d& \left(32\right)\end{array}$

(78) The target function P() and the target delay D may be selected as an input parameter to the optimization procedure. The expression P()e.sup.jwD may be referred to as the target transfer function.

(79) A measure of the energy of the total aliasing e.sub.a may be calculated by evaluating the sum of the alias terms on the right hand side (RHS) of Eq. (23), i.e. the second term of Eq. (23), on the unit circle as

(80) $\begin{array}{cc}{e}_a=\frac{1}{2}\underset{l=1}{\overset{M-1}{.Math.}}\stackrel{}{\underset{-}{}}{.Math.{\tilde{A}}_l\left(e^j\right).Math.}^{2}d,& \left(33\right)\end{array}$

(81) For real-valued systems this translates to

(82) $\begin{array}{cc}{e}_a=\frac{1}{2}\underset{l=1}{\overset{M-1}{.Math.}}\stackrel{}{\underset{-}{}}{.Math.A_l\left(e^j\right).Math.}^{2}d.& \left(34\right)\end{array}$

(83) Overall, an optimization procedure for determining a prototype filter p.sub.0(n) may be based on the minimization of the error of Eq. (30). The parameter may be used to distribute the emphasis between the transfer function and the sensitivity to aliasing of the prototype filter. While increasing the parameter towards 1 will put more emphasis on the transfer function error e.sub.t, reducing the parameter towards 0 will put more emphasis on the aliasing error e.sub.a. The parameters P() and D may be used to set a target transfer function of the prototype filter p.sub.0(n), i.e. to define the pass band and stop band behavior and to define the overall system delay.

(84) According to an example, a number of the filter bank channels k may be set to zero, e.g. the upper half of the filter bank channels are given zero gain. Consequently, the filter bank is triggered to generate a great amount of aliasing. This aliasing will be subsequently minimized by the optimization process. In other words, by setting a certain number of filter bank channels to zero, aliasing will be induced, in order to generate an aliasing error e.sub.a which may be minimized during the optimization procedure. Furthermore, computational complexity of the optimization process may be reduced by setting filter bank channels to zero.

(85) According to an example, a prototype filter is optimized for a real valued, i.e. a cosine modulated, filter bank which may be more appropriate than directly optimizing the complex-valued version.

(86) This is because real-valued processing prioritizes far-off aliasing attenuation to a larger extent than complex-valued processing. However, when triggering aliasing as outlined above, the major part of the induced aliasing in this case will typically originate from the terms carrying the main alias terms. Hence, the optimization algorithm may spend resources on minimizing the main aliasing that is inherently non-present in the resulting complex-exponential modulated system. In order to alleviate this, the optimization may be done on a partially complex system; for the alias terms which are free from main aliasing, the optimization may be done using real-valued filter processing. On the other hand, the alias terms that would carry the main alias terms in a real-valued system would be modified for complex-valued filter processing. By means of such partially complex optimization, the benefits of performing the processing using real-valued processing may be obtained, while still optimizing the prototype filter for usage in a complex modulated filter bank system.

(87) In an exemplary optimization where exactly the upper half of the filter bank channels are set to zero, the only alias term calculated from complex valued filters is the term l=M/2 of Eq. (33). In this example, the function P() of Eq. (31), may be chosen as a unit magnitude constant ranging from /2+ to /2, where is a fraction of /2, in order to cover the frequency range constituting the pass band. Outside the pass band the function P() may be defined to be zero or be left undefined. In the latter case, the error energy of the transfer function Eq. (31) is only evaluated between /2+ and /2. Alternatively and preferably, the pass band error e.sub.t could be calculated over all channels k=0, . . . , M1, from to with P() being constant, while the aliasing is still calculated with a plurality of the channels set to zero as described above.

(88) Typically the optimization procedure is an iterative procedure, where given the prototype filter coefficients p.sub.0(n)(n=0, . . . , N1) at a certain iteration step, the target delay D, the number of channels M, the numbers of low band channels set to zero loCut, the number of high band channels set to zero hiCut, and the weighting factor a, a value for the objective function for this iteration step is calculated. Using semi-complex operations, this comprises the steps: 1. To obtain the pass band error e.sub.t, evaluate Eq. (32) with P() being a constant, using

(89) $\begin{array}{cc}{A}_0\left(e^j\right)=\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left(e^j\right)F_k\left(e^j\right),& \left(35\right)\end{array}$ where H.sub.k(e.sup.j) and F.sub.k(e.sup.j) are the DFT transforms of the analysis and synthesis filters h.sub.k(n) and f.sub.k(n) as generated from the prototype filters coefficients at this iteration step from Eq. (13) to (15), respectively. 2. To obtain the aliasing error e.sub.a, for aliasing terms not subject to significant aliasing, evaluate

(90) $\begin{array}{cc}{e}_{\mathrm{aReal}}=\frac{1}{2}\underset{\underset{\underset{\underset{M-\mathrm{hiCut}}{M-\mathrm{loCut},}}{l\mathrm{loCut},\mathrm{hiCut},}}{l=1}}{\overset{M-1}{.Math.}}{}_{-}^{}{.Math.A_l\left(e^j\right).Math.}^{2}d,& \left(36\right)\end{array}$ where A.sub.l(e.sup.j) is calculated as

(91) 0$\begin{array}{cc}{A}_l\left(e^j\right)=\underset{k=\mathrm{loCut}}{\overset{M-1-\mathrm{hiCut}}{.Math.}}{H}_k\left(e^{j\left(-\frac{2}{M}l\right)}\right)F_k\left(e^j\right)& \left(37\right)\end{array}$ and H.sub.k(e.sup.j) and F.sub.k(e.sup.j) are the DFT transforms, i.e. the z-transforms evaluated on the unit circle, of the analysis and synthesis filters h.sub.k(n) and f.sub.k(n) from Eq. (13) to (15). 3. For the terms subject to significant aliasing, evaluate

(92) $\begin{array}{cc}{e}_{\mathrm{aCplx}}=\frac{1}{2}\underset{\underset{\underset{\underset{M-\mathrm{hiCut}}{M-\mathrm{loCut},}}{\mathrm{hiCut},}}{l=\mathrm{loCut},}}{.Math.}{}_{-}^{}{.Math.{\tilde{A}}_l\left(e^j\right).Math.}^{2}d& \left(38\right)\end{array}$ where .sub.l(e.sup.j) is given by Eq. (24), with A.sub.l(e.sup.j) as Eq. (37), with H.sub.k(e.sup.j) and F.sub.k(e.sup.j) being the DFT transforms of h.sub.k(n) and f.sub.k(n) from Eq. (19) and (20). 4. The error is subsequently weighted with a as
e.sub.tot()=e.sub.t+(1)(e.sub.aReal+e.sub.aCplx). (39)

(93) Using any of the nonlinear optimization algorithms referred to above, this total error is reduced by modifying the coefficients of the prototype filter, until an optimal set of coefficients is obtained. By way of example, the direction of the greatest gradient of the error function e.sub.tot is determined for the prototype filter coefficients at a given iteration step. Using a certain step size the prototype filter coefficients are modified in the direction of the greatest gradient. The modified prototype filter coefficients are used as a starting point for the subsequent iteration step. This procedure is repeated until the optimization procedure has converged to a minimum value of the error function e.sub.tot.

(94) An exemplary embodiment of the optimization procedure is illustrated in FIG. 3 as a flow diagram 300. In a parameter determination step 301 the parameters of the optimization procedure, i.e. notably the target transfer function comprising the target delay D, the number of channels M of the target filter bank, the number N of coefficients of the prototype filter, the weighting parameter of the objective error function, as well as the parameters for aliasing generation, i.e. loCut and/or hiCut, are defined. In an initialization step 302, a first set of coefficients of the prototype filter is selected.

(95) In the pass band error determination unit 303, the pass band error term e.sub.t is determined using the given set of coefficients of the prototype filter. This may be done by using Eq. (32) in combination with Eqs. (35) and (13) to (15). In the real valued aliasing error determination unit 304, a first part e.sub.aReal of the aliasing error term e.sub.a may be determined using Eqs. (36) and (37) in combination with Eqs. (13) to (15). Furthermore, in the complex valued aliasing error determination unit 305, a second part e.sub.aCplx of the aliasing error term e.sub.a may be determined using Eq. (38) in combination with Eqs. (19) and (20). As a consequence, the objective function e.sub.tot may be determined from the results of the units 303, 304 and 305 using Eq. (39).

(96) The nonlinear optimization unit 306 uses optimization methods, such as linear programming, in order to reduce the value of the objective function. By way of example, this may be done by determining a possibly maximum gradient of the objective function with regards to modifications of the coefficients of the prototype filter. In other words, those modifications of the coefficients of the prototype filter may be determined which result in a possibly maximum reduction of the objective function.

(97) If the gradient determined in unit 306 remains within predetermined bounds, the decision unit 307 decides that a minimum of the objective function has been reached and terminates the optimization procedure in step 308. If on the other hand, the gradient exceeds the predetermined value, then the coefficients of the prototype filter are updated in the update unit 309. The update of the coefficients may be performed by modifying the coefficients with a predetermined step into the direction given by the gradient. Eventually, the updated coefficients of the prototype filter are reinserted as an input to the pass band error determination unit 303 for another iteration of the optimization procedure.

(98) Overall, it can be stated that using the above error function and an appropriate optimization algorithm, prototype filters may be determined that are optimized with respect to their degree of perfect reconstruction, i.e. with respect to low aliasing in combination with low phase and/or amplitude distortion, their resilience to aliasing due to subband modifications, their system delay and/or their transfer function. The design method provides parameters, notably a weighting parameter , a target delay D, a target transfer function P(), a filter length N, a number of filter bank channels M, as well as aliasing trigger parameters hiCut, loCut, which may be selected to obtain an optimal combination of the above mentioned filter properties. Furthermore, the setting to zero of a certain number of subband channels, as well as the partial complex processing may be used to reduce the overall complexity of the optimization procedure. As a result, asymmetric prototype filters with a near perfect reconstruction property, low sensitivity to aliasing and a low system delay may be determined for usage in a complex exponential modulated filter bank. It should be noted that the above determination scheme of a prototype filter has been outlined in the context of a complex exponential modulated filter bank. If other filter bank design methods are used, e.g. cosine modulated or sine modulated filter bank design methods, then the optimization procedure may be adapted by generating the analysis and synthesis filters h.sub.k(n) and f.sub.k(n) using the design equations of the respective filter bank design method. By way of example, Eqs. (13) to (15) may be used in the context of a cosine modulated filter bank.

(99) In the following, a detailed example of a 64 channel low delay filter bank is described. Using the proposed aforementioned optimization method, a detailed example of an alias gain term optimized, low delay, 64-channel filter bank (M=64) will be outlined. In this example the partially complex optimization method has been used and the uppermost 40 channels have been set to zero during the prototype filter optimization, i.e. hiCut=40, whereas the loCut parameter remained unused. Hence, all alias gain terms, except .sub.l, where l=24, 40, are calculated using real-valued filters. The total system delay is chosen as D=319, and the prototype filter length is N=640. A time domain plot of the resulting prototype filter is given in FIG. 4(a), and the frequency response of the prototype filter is depicted in FIG. 4(b). The filter bank offers a pass band (amplitude and phase) reconstruction error of 72 dB. The phase deviation from a linear phase is smaller than 0.02, and the aliasing suppression is 76 dB when no modifications are done to the subband samples. The actual filter coefficients are tabulated in Table 1. Note that the coefficients are scaled by a factor M=64 in respect to other equations in this document that are dependent on an absolute scaling of the prototype filter.

(100) While the above description of the design of the filter bank is based on a standard filter bank notation, an example for operating the designed filter bank may operate in other filter bank descriptions or notations, e.g. filter bank implementations which allow a more efficient operation on a digital signal processor.

(101) In an example, the steps for filtering a time domain signal using the optimized prototype filter may be described as follows: In order to operate the filter bank in an efficient manner, the prototype filter, i.e. p.sub.0(n) from Table 1, is first arranged in the poly-phase representation, where every other of the poly-phase filter coefficients are negated and all coefficient are time-flipped as
p.sub.0(639128mn)=(1).sup.m p.sub.0(128m+n), 0n<128, 0m<5 (40) The analysis stage begins with the poly-phase representation of the filter being applied to the time domain signal x(n) to produce a vector x.sub.i(n) of length 128 as

(102) $\begin{array}{cc}{x}_{127-l}\left(n\right)=\underset{m=0}{\overset{4}{.Math.}}{p}_0^{}\left(128m+l\right)x\left(128m+l+64n\right),0l<128,n=0,1,\mathrm{.Math.}& \left(41\right)\end{array}$

(103) x.sub.l(n) is subsequently multiplied with a modulation matrix as

(104) $\begin{array}{cc}{v}_k\left(n\right)=\underset{l=0}{\overset{127}{.Math.}}{x}_l\left(n\right)\exp \left(j\frac{}{128}\left(k+\frac{1}{2}\right)\left(2l+129\right)\right),0k<64,& \left(42\right)\end{array}$ where v.sub.k(n), k=0 . . . 63, constitute the subband signals. The time index n is consequently given in subband samples. The complex-valued subband signals can then be modified, e.g. according to some desired, possibly time-varying and complex-valued, equalization curve g.sub.k(n), as
v.sub.k.sup.(m)(n)=g.sub.k(n)v.sub.k(n), 0k<64. (43) The synthesis stage starts with a demodulation step of the modified subband signals as

(105) $\begin{array}{cc}{u}_l\left(n\right)=\frac{1}{64}\underset{k=0}{\overset{63}{.Math.}}\mathrm{Re}\left\{v_k^{\left(m\right)}\left(n\right)\exp \left(j\frac{}{128}\left(k+\frac{1}{2}\right)\left(2l-255\right)\right)\right\},0l<128.& \left(44\right)\end{array}$ It should be noted that the modulation steps of Eqs. (42) and (44) may be accomplished in a computationally very efficient manner with fast algorithms using fast Fourier transform (FFT) kernels. The demodulated samples are filtered with the poly-phase representation of the prototype filter and accumulated to the output time domain signal 2(n) according to
{circumflex over (x)}(128m+l+64n)={circumflex over (x)}(128m+l+64n)+p.sub.0(639128ml)u.sub.l(l), 0l<128, 0m<5, n=0,1, (45) where {circumflex over (x)}(n) is set to 0 for all n at start-up time.

(106) It should be noted that both floating point and fixed point implementations might change the numerical accuracy of the coefficients given in Table 1 to something more suitable for processing.

(107) Without limiting the scope, the values may be quantized to a lower numerical accuracy by rounding, truncating and/or by scaling the coefficients to integer or other representations, in particular representations that are adapted to the available resources of a hardware and/or software platform on which the filter bank is to operate.

(108) Moreover, the example above outlines the operation where the time domain output signal is of the same sampling frequency as the input signal. Other implementations may resample the time domain signal by using different sizes, i.e. different number of channels, of the analysis and synthesis filter banks, respectively. However, the filter banks should be based on the same prototype filter, and are obtained by resampling of the original prototype filter through either decimation or interpolation. As an example, a prototype filter for a 32 channel filter bank is achieved by resampling the coefficients p.sub.0(n) as
p.sub.0.sup.(32)(i)=[p.sub.0(2i+1)+p.sub.0(2i)], 0i<320.

(109) The length of the new prototype filter is hence 320 and the delay is D=319/2=159, where the operator . returns the integer part of its argument.

(110) TABLE-US-00001 TABLE 1 Coefficients of a 64 channel low delay prototype filter n P.sub.0(n) 0 7.949261005955764e4 1 1.232074328145439e3 2 1.601053942982895e3 3 1.980720409470913e3 4 2.397504953865715e3 5 2.838709203607079e3 6 3.314755401090670e3 7 3.825180949035082e3 8 4.365307413613105e3 9 4.937260935539922e3 10 5.537381514710146e3 11 6.164241937824271e3 12 6.816579194002503e3 13 7.490102145765528e3 14 8.183711450708110e3 15 8.894930051379498e3 16 9.620004581607449e3 17 1.035696814015217e2 18 1.110238617202191e2 19 1.185358556146692e2 20 1.260769256679562e2 21 1.336080675156018e2 22 1.411033176541011e2 23 1.485316243134798e2 24 1.558550942227883e2 25 1.630436835497356e2 26 1.700613959422392e2 27 1.768770555992799e2 28 1.834568069395711e2 29 1.897612496482356e2 30 1.957605813345359e2 31 2.014213322475170e2 32 2.067061748933033e2 33 2.115814831921453e2 34 2.160130854695980e2 35 2.199696217022438e2 36 2.234169110698344e2 37 2.263170795250229e2 38 2.286416556008894e2 39 2.303589449043864e2 40 2.314344724218223e2 41 2.318352524475873e2 42 2.315297727620401e2 43 2.304918234544422e2 44 2.286864521420490e2 45 2.260790764376614e2 46 2.226444264459477e2 47 2.183518667784246e2 48 2.131692017682024e2 49 2.070614962636994e2 50 1.999981321635736e2 51 1.919566223498554e2 52 1.828936158524688e2 53 1.727711874492186e2 54 1.615648494779686e2 55 1.492335807272955e2 56 1.357419760297910e2 57 1.210370330110896e2 58 1.050755164953818e2 59 8.785746151726750e3 60 6.927329556345040e3 61 4.929378450735877e3 62 2.800333941149626e3 63 4.685580749545335e4 64 2.210315255690887e3 65 5.183294908090526e3 66 8.350964449424035e3 67 1.166118535611788e2 68 1.513166797475777e2 69 1.877264877027943e2 70 2.258899222368603e2 71 2.659061474958830e2 72 3.078087745385930e2 73 3.516391224752870e2 74 3.974674893613862e2 75 4.453308211110493e2 76 4.952626097917320e2 77 5.473026727738295e2 78 6.014835645056577e2 79 6.578414516120631e2 80 7.163950999489413e2 81 7.771656494569829e2 82 8.401794441130064e2 83 9.054515924487507e2 84 9.729889691289549e2 85 1.042804039148369e1 86 1.114900795290448e1 87 1.189284254931251e1 88 1.265947532678997e1 89 1.344885599112251e1 90 1.426090972422485e1 91 1.509550307914161e1 92 1.595243494708706e1 93 1.683151598707939e1 94 1.773250461581686e1 95 1.865511418631904e1 96 1.959902227114119e1 97 2.056386275763479e1 98 2.154925974105375e1 99 2.255475564993390e1 100 2.357989864681126e1 101 2.462418809459464e1 102 2.568709554604541e1 103 2.676805358910440e1 104 2.786645734207760e1 105 2.898168394038287e1 106 3.011307516871287e1 107 3.125994749246541e1 108 3.242157192666507e1 109 3.359722796803192e1 110 3.478614117031655e1 111 3.598752336287570e1 112 3.720056632072922e1 113 3.842444358173011e1 114 3.965831241942321e1 115 4.090129566893579e1 116 4.215250930838456e1 117 4.341108982328533e1 118 4.467608231633283e1 119 4.594659376709624e1 120 4.722166595058233e1 121 4.850038204075748e1 122 4.978178235802594e1 123 5.106483456192374e1 124 5.234865375971977e1 125 5.363218470709771e1 126 5.491440356706657e1 127 5.619439923555571e1 128 5.746001351404267e1 129 5.872559277139351e1 130 5.998618924353250e1 131 6.123980151490041e1 132 6.248504862282382e1 133 6.372102969387355e1 134 6.494654463921502e1 135 6.616044277534099e1 136 6.736174463977084e1 137 6.854929931488056e1 138 6.972201618598393e1 139 7.087881675504216e1 140 7.201859881692665e1 141 7.314035334082558e1 142 7.424295078874311e1 143 7.532534422335129e1 144 7.638649113306198e1 145 7.742538112450130e1 146 7.844095212375462e1 147 7.943222347831999e1 148 8.039818519286321e1 149 8.133789939828571e1 150 8.225037151897938e1 151 8.313468549324594e1 152 8.398991600556686e1 153 8.481519810689574e1 154 8.560963550316389e1 155 8.637239863984174e1 156 8.710266607496513e1 157 8.779965198108476e1 158 8.846258145496611e1 159 8.909071890560218e1 160 8.968337036455653e1 161 9.023985431182168e1 162 9.075955881221292e1 163 9.124187296760565e1 164 9.168621399784253e1 165 9.209204531389191e1 166 9.245886139655739e1 167 9.278619263447355e1 168 9.307362242659798e1 169 9.332075222986479e1 170 9.352724511271509e1 171 9.369278287932853e1 172 9.381709878904797e1 173 9.389996917291260e1 174 9.394121230559878e1 175 9.394068064126931e1 176 9.389829174860432e1 177 9.381397976778112e1 178 9.368773370086998e1 179 9.351961242404785e1 180 9.330966718935136e1 181 9.305803205049067e1 182 9.276488080866625e1 183 9.243040558859498e1 184 9.205488097488350e1 185 9.163856478189402e1 186 9.118180055332041e1 187 9.068503557855540e1 188 9.014858673099563e1 189 8.957295448806664e1 190 8.895882558527375e1 191 8.830582442418677e1 192 8.761259906419252e1 193 8.688044201931157e1 194 8.611140376567749e1 195 8.530684188588082e1 196 8.446723286380624e1 197 8.359322523144003e1 198 8.268555005748937e1 199 8.174491260941859e1 200 8.077214932837783e1 201 7.976809997929416e1 202 7.873360271773119e1 203 7.766956604639097e1 204 7.657692341138960e1 205 7.545663748526984e1 206 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1.642633580824677e3 535 1.704200291375062e3 536 1.760514312756149e3 537 1.811458673156579e3 538 1.856981580032126e3 539 1.897029046447624e3 540 1.931585942699363e3 541 1.960627084932276e3 542 1.984178530495641e3 543 2.002288840866127e3 544 2.014916352347506e3 545 2.022189226793424e3 546 2.024254777335021e3 547 2.021156706871573e3 548 2.013111787438794e3 549 2.000212633130633e3 550 1.982687042477966e3 551 1.960693892404943e3 552 1.934407806173517e3 553 1.904123563599214e3 554 1.870072199436830e3 555 1.832519954023970e3 556 1.791756667369466e3 557 1.747978720577777e3 558 1.701541033746949e3 559 1.652689459435072e3 560 1.601690868666912e3 561 1.548954090992685e3 562 1.494709797777335e3 563 1.439190571857024e3 564 1.382763830841281e3 565 1.325642967049430e3 566 1.268184236874211e3 567 1.210596701555163e3 568 1.153025111297160e3 569 1.095962010293130e3 570 1.039553843860894e3 571 9.838346246983619e4 572 9.290281181623759e4 573 8.749810533387956e4 574 8.215803921619577e4 575 7.706114369075383e4 576 7.240453976226097e4 577 6.849432723864428e4 578 6.499492788836954e4 579 6.169265465797999e4 580 5.864023580206857e4 581 5.585564628691223e4 582 5.332623456777386e4 583 5.106711356117643e4 584 4.907668696713635e4 585 4.734587422398502e4 586 4.585871522474066e4 587 4.460035977692689e4 588 4.356377129231574e4 589 4.273247732616044e4 590 4.208333621911742e4 591 4.159437129295563e4 592 4.123958508631197e4 593 4.100224176114866e4 594 4.085466400930828e4 595 4.077080867389932e4 596 4.073254606881664e4 597 4.070933269997811e4 598 4.067607615013048e4 599 4.061488056951641e4 600 4.050555465493161e4 601 4.033838274959328e4 602 4.008810861049167e4 603 3.973769462134710e4 604 3.928186163645286e4 605 3.870561868619109e4 606 3.799993669990150e4 607 3.715971708042990e4 608 3.617549303005874e4 609 3.505340232816606e4 610 3.378810708512397e4 611 3.237820254163679e4 612 3.083797394566325e4 613 2.916580376245428e4 614 2.737128656378774e4 615 2.546266898474145e4 616 2.344785058384558e4 617 2.134575242388197e4 618 1.916264055195752e4 619 1.692851860592005e4 620 1.466953561242506e4 621 1.236855725370398e4 622 1.005737421222391e4 623 7.750656629326379e5 624 5.466984383016220e5 625 3.255925659037227e5 626 1.096860208856302e5 627 9.881411051921578e6 628 2.951496818998434e5 629 4.810106298036608e5 630 6.513783951460106e5 631 8.051456871678129e5 632 9.429776656872437e5 633 1.058298511976110e4 634 1.155823148740170e4 635 1.229659417867084e4 636 1.266886375085138e4 637 1.279376783418106e4 638 1.216914974923773e4 639 9.386301157644215e5

(111) In the following, different aspects of practical implementations are outlined. Using a standard PC or DSP, real-time operation of a low delay complex-exponential modulated filter bank is possible. The filter bank may also be hard-coded on a custom chip. FIG. 5(a) shows the structure for an effective implementation of the analysis part of a complex-exponential modulated filter bank system. The analogue input signal is first fed to an A/D converter 501. The digital time domain signal is fed to a shift register holding 2M samples shifting M samples at a time 502. The signals from the shift register are then filtered through the poly-phase coefficients of the prototype filter 503. The filtered signals are subsequently combined 504 and in parallel transformed with a DCT-IV 505 and a DST-IV 506 transform. The outputs from the cosine and sine transforms constitute the real and the imaginary parts of the subband samples respectively. The gains of the subband samples are modified according to the current spectral envelope adjuster setting 507.

(112) An effective implementation of the synthesis part of a low delay complex-exponential modulated system is shown in FIG. 5(b). The subband samples are first multiplied with complex-valued twiddle-factors, i.e. complex-valued channel dependent constants, 511, and the real part is modulated with a DCT-IV 512 and the imaginary part with a DST-IV 513 transform. The outputs from the transforms are combined 514 and fed through the poly-phase components of the prototype filter 515. The time domain output signal is obtained from the shift register 516. Finally, the digital output signal is converted back to an analogue waveform 517.

(113) While the above outlined implementations use DCT and DST type IV transforms, implementations using DCT type II and III kernels are equally possible (and also DST type II and III based implementations). However, the most computationally efficient implementations for complex-exponential modulated banks use pure FFT kernels. Implementations using a direct matrix-vector multiplication are also possible but are inferior in efficiency.

(114) In summary, the present document describes a design method for prototype filters used in analysis/synthesis filter banks. Desired properties of the prototype filters and the resulting analysis/synthesis filter banks are near perfect reconstruction, low delay, low sensitivity to aliasing and minimal amplitude/phase distortion. An error function is proposed which may be used in an optimization algorithm to determine appropriate coefficients of the prototype filters. The error function comprises a set of parameters that may be tuned to modify the emphasis between the desired filter properties. Preferably, asymmetric prototype filters are used. Furthermore, a prototype filter is described which provides a good compromise of desired filter properties, i.e. near perfect reconstruction, low delay, high resilience to aliasing and minimal phase/amplitude distortion.

(115) Some embodiments and applications may comprise a plurality of analysis filter banks arranged in series. For example, in the embodiment shown in FIG. 6, an audio signal processing chain 600 comprises an analysis section 605, downstream of which is a signal processing section 610, downstream of which is a synthesis section 615. The analysis section 605 comprises a decimated modulated filter bank 620, downstream of which is a secondary modulated filter section 625. In the present embodiment, the secondary modulated filter section 625 comprises a plurality of Nyquist filter banks, as will be described below with reference to FIG. 7, for the purpose of increasing the spectral resolution of a low-frequency part of the output of the decimated modulated filter bank 620. In other embodiments, the secondary modulated filter section 625 may comprise filter banks of a different type, selected for suitability to the desired purpose of secondary modulated filter section 625 in those embodiments.

(116) The analysis section 605 is configured to receive a time-domain representation of an audio signal, which in this embodiment comprises pulse code modulated (PCM) audio data, and to output a frequency-domain representation of the audio signal, which in this embodiment comprises sixty-four subbands of transform domain audio data. The analysis section 605 is described in more detail below, after describing the signal processing section 610 and the synthesis section 615.

(117) The signal processing section 610 is configured to receive the frequency-domain representation of the audio signal from the analysis section 605, to process it in a desired manner, and to output the processed frequency-domain representation of the audio signal. Examples of processing which may be performed by the signal processing section 610 include equalization, volume leveling, upmixing or the like.

(118) The synthesis section 615 is configured to receive the processed frequency-domain representation from the signal processing section 610. The synthesis section 615 comprises a decimated modulated filter bank 630 which is matched to, and the inverse of, the decimated modulated filter bank 620 in the analysis section 605. The decimated modulated filter bank 630 is configured to transform the processed frequency-domain representation into a processed time-domain representation of the audio signal, which in this embodiment comprises PCM audio data. The synthesis section 615 outputs the processed time-domain representation of the audio signal.

(119) As shown in FIG. 7, the analysis section's decimated modulated filter bank 620 comprises a plurality of filters in parallel (in the present embodiment, sixty-four filters in parallel), each of which is designed using a prototype filter obtained as described above. The decimated modulated filter bank 620 includes a plurality of decimators in parallel (in the present embodiment, sixty-four decimators in parallel), each decimator receiving the output of a respective one of the parallel filters. The output of the decimators, and the decimated modulated filter bank 620, is a plurality of subbands of frequency-domain audio data (in the present embodiment, sixty-four subbands). In the present embodiment, these subbands of frequency-domain audio data overlap their respective neighboring subband(s) and all are of equal width (e.g., sixty-four subbands centered, respectively, on the intervals 0-375 Hz, 375-750 Hz, . . . , 23.625-24 kHz).

(120) The analysis section's Nyquist filter section 625 comprises a plurality of Nyquist filter banks 705, 710 (in the present embodiment, three Nyquist filter banks in parallel) and a plurality of delay-compensation modules 715 (in the present embodiment, sixty-one delay-compensation modules in parallel), all arranged in parallel. The skilled person will recognize that a Nyquist filter bank is a modulated filter bank designed using a Nyquist prototype filter.

(121) The plurality of Nyquist filter banks 705, 710, in the present embodiment, consists of a bank of eight filters 705 and two banks or fours filter 710. The eight-filter bank 705 receives exactly one of the subbands of frequency-domain audio data from the analysis section's decimated modulated filter bank 620, namely the lowest-frequency subband (e.g., 0-375 Hz), and divides it into eight subbands of frequency-domain audio data. Each one of the four-filter banks 710 also receives exactly one of the subbands of frequency-domain audio data from the analysis section's decimated modulated filter bank 620, namely the second- and third-lowest-frequency subbands, respectively (e.g., 375-750 Hz and 0.75-1.125 kHz, respectively), and divides it into four subbands of frequency-domain audio data. The delay-compensation modules 715 receive the remainder of the subbands of frequency-domain audio data from the analysis section's decimated modulated filter bank 620, with each one of the delay-compensation modules 715 receiving a respective one of the subbands. It will be appreciated that the Nyquist filter banks 705, 710 serve to increase the spectral resolution of a low-frequency part of the frequency-domain audio data generated by the decimated modulated filter bank 620.

(122) In the present embodiment, the delay-compensation modules 715 simply output delayed versions of the received subbands of frequency-domain audio data, delayed by an amount suitable to compensate for the group delay introduced by the Nyquist filter banks 705, 710. In other embodiments, the delay-compensation modules 715 may also apply a non-unity gain.

(123) The Nyquist filter banks 705, 710 are designed using two Nyquist prototype filters, a first Nyquist prototype filter for the eight-filter bank 705 and a second Nyquist prototype filter for the two four-filter banks 710. The lengths of the first and second Nyquist prototype filters are both 11. The group delays of both the first and second Nyquist prototype filters are the same, namely 3 samples. The coefficients of the first and second Nyquist prototype filters are shown below in Tables 2 and 3, respectively.

(124) TABLE-US-00002 TABLE 2 Coefficients of first Nyquist prototype filter n P.sub.0(n) 0 6.1756816E01 1 4.1611960E01 2 9.5586320E01 3 1.0000000E+00 4 9.5685520E01 5 8.9936720E01 6 7.9687024E01 7 6.3185832E01 8 4.2794616E01 9 2.3148472E01 10 8.3106320E02

(125) TABLE-US-00003 TABLE 3 Coefficients of second Nyquist prototype filter n P.sub.0(n) 0 2.9776896E01 1 5.6090560E01 2 9.3182600E01 3 1.0000000E+00 4 9.3181680E01 5 6.5644120E01 6 2.8636192E01 7 0.0 8 1.1892176E01 9 1.0686788E01 10 4.9179360E02

(126) It will be readily appreciated from Tables 2 and 3, that both of the Nyquist prototype filters are asymmetric. Using asymmetric Nyquist prototype filters enabled the design of filters which achieve a desirable frequency response (with relatively steep frequency roll off) notwithstanding their relatively low group delays, and so their group delays could be reduced without overly sacrificing on their performance in terms of band separation between adjacent filters of the Nyquist filter banks 705, 710. Although the computational complexity of the Nyquist filter banks 705, 710 is low relative to that of the decimated modulated filter bank 620, the group delay of the Nyquist filter banks 705, 710 is significant as a result of their operating on decimated (i.e., lower sampling rate) frequency-domain audio data, particularly when the decimators of the decimated modulated filter bank 620 have a high decimation factor. Therefore, by targeting the secondary filter section 625, which may be viewed as a relatively minor part of the analysis section 605, it was possible to obtain a substantial reduction in end-to-end latency, which highly desirable in some applications, such as real-time communications applications like Voice over Internet Protocol (VoIP) teleconferencing.

(127) Alternatively, since the Nyquist prototype filters each have far fewer coefficients than the prototype filter with which the method 300 is concerned (in the present embodiment, 11 coefficients as compared with 640 coefficients), the skilled person is capable of computing suitable coefficients for the asymmetric Nyquist prototype filters using a variety of different filter-design methods or tools known to those skilled in the art. More information on Nyquist filter banks may be found, for example, in Multirate Systems and Filter Banks, by Vaidyanathan, P. P., ISBN 9780136057185, 1993, Prentice Hall, in Chapter 4.6.

(128) A general method 800 of determining suitable coefficients for the first and second Nyquist prototype filters is shown in FIG. 8, which generally speaking is an adaptation of the method 300 which is shown in FIG. 3 and is described above. The method 800 of determining suitable coefficients for the first and second Nyquist prototype filters comprises a parameter determination step 801, an initialization step 802, a band-independence determination step 803, an optimization step 806, a decision step 807, a coefficient update step 809 and a termination step 808. The parameter determination step 801, initialization step 802, decision step 807, coefficient updates step 809 and termination step 808 are equivalent, respectively, to the like-named steps 301, 302, 307, 309, 308 of the method 300. In the method 800 no equivalents to steps 304 and 35 of the method 300 are included, because aliasing error is not an issue for the Nyquist filter banks.

(129) The band-independence determination step 803 comprises determining an objective function. The objective function comprises a band separation term error term associated with the deviation between the separation of the respective subbands output by adjacent filter in the Nyquist filter bank, and a target separation of these subbands. No specific formulation of the objective function is essential.

(130) The optimization step 806 uses optimization methods, such as linear programming, in order to reduce the value of the objective function while meeting a set of constraints. The set of constraints includes the following constraints: meeting a desired group delay, for example 3 samples; and meeting the typical characteristics of a Nyquist filter (such as having coefficients of zero at the required intervals). By way of example, this may be done by determining a possibly maximum gradient of the objective function with regards to modifications of the coefficients of the prototype filter. In other words, those modifications of the coefficients of the prototype filter may be determined which result in a possibly maximum reduction of the objective function.

(131) If the gradient determined in unit 806 remains within predetermined bounds, the decision unit 807 decides that a minimum of the objective function has been reached and terminates the method in step 808. If on the other hand, the gradient exceeds the predetermined value, then the coefficients of the prototype filter are updated in the coefficient update step 809. The update of the coefficients may be performed by modifying the coefficients with a predetermined step into the direction given by the gradient. Eventually, the updated coefficients of the Nyquist prototype filter are reinserted as an input to the band-independence determination step 803 for another iteration of the optimization procedure.

(132) While specific embodiments and applications have been described herein, it will be apparent to those of ordinary skill in the art that many variations on the embodiments and applications described herein are possible without departing from the scope of the invention described and claimed herein.

(133) It should be understood that while certain forms of the invention have been shown and described, the invention is not to be limited to the specific embodiments described and shown or the specific methods described.

(134) The filter design method and system as well as the filter bank described in the present document may be implemented as software, firmware and/or hardware. Certain components may e.g. be implemented as software running on a digital signal processor or microprocessor. Other component may e.g. be implemented as hardware and or as application specific integrated circuits.

(135) In various embodiments, an apparatus falling within the scope of the appended claims comprises a special-purpose computing device. The computing device includes an interface system. The interface system may include a network interface, such as a wireless network interface. Alternatively, or additionally, the interface system may include a universal serial bus (USB) interface or another such interface. The device includes a logic system. The logic system may include a processor, such as a general purpose single- or multi-chip processor. The logic system may include a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components, or combinations thereof. The logic system may be configured to control the other components of the device. The logic system may be configured with interfaces for communication with the other components. The other components may or may not be configured for communication with one another, as appropriate. The logic system may be configured to perform data processing functionality, including but not limited to the techniques described herein. In some such implementations, the logic system may be configured to operate (at least in part) according to software, such as a computer program, stored one or more non-transitory media. The non-transitory media may include memory associated with the logic system, such as random access memory (RAM) and/or read-only memory (ROM). The non-transitory media may include memory of a memory system. The memory system may include one or more suitable types of non-transitory storage media, such as flash memory, a hard drive, etc.

(136) The signals encountered in the described methods and systems may be stored on media such as random access memory or optical storage media. They may be transferred via networks, such as radio networks, satellite networks, wireless networks or wireline networks, e.g. the Internet. Typical devices making use of the filter banks described in the present document are set-top boxes or other customer premises equipment which decode audio signals. On the encoding side, the filter banks may be used in broadcasting stations, e.g. in video headend systems.

(137) It will be appreciated that various aspects of the present disclosure provide, inter-alia, the following numbered embodiments. 1. A method (300) for determining N coefficients of an asymmetric prototype filter p.sub.0 for usage for building an M-channel, low delay decimated analysis/synthesis filter bank (101, 102) comprising M analysis filters h.sub.k (103) and M synthesis filters f.sub.k (106), k=0, . . . , M1, wherein M is greater than 1, and having a transfer function, the method comprising the steps of choosing (301) a target transfer function of the filter bank comprising a target delay D; wherein D is smaller or equal to N; determining (303, 304, 305) a composite objective function e.sub.tot comprising a pass band error term e.sub.t and an aliasing error term e.sub.a; wherein the pass band error term e.sub.t is associated with the deviation between the transfer function of the filter bank and the target transfer function; and wherein the aliasing error term e.sub.a is associated with errors incurred due to the subsampling of the filter bank; and determining (306, 309) N coefficients of the asymmetric prototype filter p.sub.0 that reduce the composite objective function e.sub.tot. 2. The method (300) of embodiment 1, wherein the composite objective function e.sub.tot is given by:
e.sub.tot()=e.sub.t+(1)e.sub.a, with being a weighting constant taking on values between 0 and 1. 3. The method (300) of embodiment 2, wherein the pass band error term e.sub.t is determined (303) by accumulating the squared deviation between the transfer function of the filter bank and the target transfer function for a plurality of frequencies. 4. The method (300) of embodiment 3, wherein the pass band error term e.sub.t is calculated (303) as

(138) $e_t=\frac{1}{2}{}_{-}^{}{.Math.\frac{1}{2}\left(A_0\left(e^j\right)+A_0^{*}\left(e^{-j}\right)\right)-P\left(\right)e^{-jD}.Math.}^{2}d,$ with P()e.sup.jD being the target transfer function, and

(139) $A_0\left(z\right)=\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left(z\right)F_k\left(z\right),$ wherein H.sub.k(z) and F.sub.k(z) are the z-transforms of the analysis and synthesis filters h.sub.k(n) and f.sub.k(n), respectively. 5. The method (300) of embodiment 3, wherein the aliasing error term e.sub.a is determined (304, 305) by accumulating the squared magnitude of alias gain terms for a plurality of frequencies. 6. The method (300) of embodiment 5, wherein the aliasing error term e.sub.a is calculated (304, 305) as

(140) $e_a=\frac{1}{2}\underset{l=1}{\overset{M-1}{.Math.}}{}_{-}^{}{.Math.{\tilde{A}}_l\left(e^j\right).Math.}^{2}d,$ with .sub.l(z)=(z)+A.sub.Ml*(z), l=1 . . . M1, and z=e.sup.j and with

(141) $A_l\left(z\right)=\underset{k=0}{\overset{M-1}{.Math.}}{H}_k\left(z{W}^l\right)F_k\left(z\right)$ being the l.sup.th alias gain term evaluated on the unit circle with W=e.sup.2/M, wherein H.sub.k(z) and F.sub.k(z) are the z-transforms of the analysis and synthesis filters h.sub.k(n) and f.sub.k(n), respectively. 7. The method (300) of any previous numbered embodiment, wherein the step of determining (303, 304, 305) the composite objective function e.sub.tot and the step of determining (306, 309) the N coefficients of the asymmetric prototype filter p.sub.0 are repeated iteratively, until a minimum of the composite objective function e.sub.tot is reached. 8. The method (300) of embodiment 7, wherein the step of determining (303, 304, 305) the composite objective function e.sub.tot comprises determining a value for the composite objective function e.sub.tot for given coefficients of the prototype filter p.sub.0; and the step of determining (306, 309) the N coefficients of the asymmetric prototype filter p.sub.0 comprises determining (309) updated coefficients of the prototype filter p.sub.0 based on the gradient of the composite objective function e.sub.tot with respect to the coefficients of the prototype filter p.sub.0. 9. The method (300) of any previous numbered embodiment, wherein the step of determining (303, 304, 305) a value for the composite objective function e.sub.tot comprises: generating the analysis filters h.sub.k and the synthesis filters f.sub.k of the analysis/synthesis filter bank based on the prototype filter p.sub.0 using cosine modulation, sine modulation and/or complex-exponential modulation. 10. The method (300) of any previous numbered embodiment, wherein the analysis and synthesis filters are determined as

(142) $h_k\left(n\right)=\sqrt{2}{p}_0\left(n\right)\cos \left\{\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}\frac{M}{2}\right)\right\},$ with n=0 . . . N1, for the M analysis filters of the analysis filter bank and;

(143) 0$f_k\left(n\right)=\sqrt{2}{p}_0\left(n\right)\cos \left\{\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}\frac{M}{2}\right)\right\},$ with n=0 . . . N1, for the M synthesis filters of the synthesis filter bank. 11. The method (300) of any previous numbered embodiment, wherein the analysis and synthesis filters are determined as

(144) $h_k\left(n\right)=p_0\left(n\right)\exp \left\{i\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}-A\right)\right\},$ with n=0 . . . N1, and A being an arbitrary constant, for the M analysis filters of the analysis filter bank and;

(145) $f_k\left(n\right)=p_0\left(n\right)\exp \left\{i\frac{}{M}\left(k+\frac{1}{2}\right)\left(n-\frac{D}{2}+A\right)\right\},$ with n=0 . . . N1, for the M synthesis filters of the synthesis filter bank. 12. The method (300) of any previous numbered embodiment, wherein the step of determining (303, 304, 305) a value for the composite objective function e.sub.tot comprises: setting at least one of the filter bank channels to zero. 13. The method (300) of embodiment 12, wherein the low frequency filter bank channels k=0 up to C.sub.low; with C.sub.low greater than zero and/or the high frequency filter bank channels k=C.sub.high up to M1, with C.sub.high smaller than M1, are set to zero. 14. The method (300) of embodiment 13, wherein the step of determining (303, 304, 305) a value for the composite objective function e.sub.tot comprises: generating the analysis and synthesis filters for the channels C.sub.low and MC.sub.low and/or C.sub.high and MC.sub.high using embodiment 11; and generating the analysis and synthesis filters for the remaining channels using embodiment 10. 15. The method (300) of any previous numbered embodiment, wherein the analysis filter bank generates M subband signals from an input signal using the M analysis filters h.sub.k (103); the M subband signals are decimated (104) by a factor M, yielding decimated subband signals; the decimated subband signals are upsampled (105) by a factor M; and the synthesis filter bank generates an output signal from the upsampled decimated subband signals using the M synthesis filters f.sub.k (106). 16. The method (300) of any previous numbered embodiment, wherein M=64; N=640; and D=319. 17. An asymmetric prototype filter p.sub.0(n) comprising coefficients derivable from the coefficients of Table 1 by any of the operations of rounding, truncating, scaling, subsampling or oversampling. 18. A filter bank comprising M filters based on the asymmetric prototype filter according to embodiment 17 and/or based on an asymmetric prototype filter generated by the method of any of embodiments 1 to 16. 19. The filter bank according to embodiment 18, wherein the M filters are modulated versions of the prototype filter; and the modulation is a cosine modulation, sine modulation and/or complex-exponential modulation. 20. A method for generating decimated subband signals with low sensitivity to aliasing emerging from modifications of said subband signals, the method comprising the steps of: determining analysis filters of an analysis/synthesis filter bank according to embodiments 18 or 19; filtering a real-valued time domain signal through said analysis filters, to obtain complex-valued subband signals; and decimating said subband signals. 21. A method for generating a real valued output signal from a plurality of complex-valued subband signals with low sensitivity to aliasing emerging from modifications of said subband signals, the method comprising the steps of: determining synthesis filters of an analysis/synthesis filter bank according to embodiments 18 or 19; interpolating said plurality of complex-valued subband signals; filtering said plurality of interpolated subband signals through said synthesis filters; generating a complex-valued time domain output signal as the sum of the signals obtained from said filtering; and taking the real part of the complex-valued time domain output signal as the real valued output signal. 22. A system operative of generating subband signals from a time domain input signal, wherein the system comprises an analysis filter bank according to embodiments 18 or 19. 23. An apparatus operable to determining N coefficients of an asymmetric prototype filter p.sub.0 for usage for building an M-channel, low delay decimated analysis/synthesis filter bank (101, 102) comprising M analysis filters h.sub.k (103) and M synthesis filters f.sub.k (106), k=0, . . . , M1, wherein M is greater than 1, and having a transfer function, the apparatus comprising a parameter setting unit (301) operable to setting a target transfer function of the filter bank comprising a target delay D; wherein D is smaller or equal to N; an error determination unit (303, 304, 305) operable to determining a composite objective function e.sub.tot comprising a pass band error term e.sub.t and an aliasing error term e.sub.a; wherein the pass band error term e.sub.t is associated with the deviation between the transfer function of the filter bank and the target transfer function; and wherein the aliasing error term e.sub.a is associated with errors incurred due to the subsampling of the filter bank; and an optimization unit (306, 309) operable to determining N coefficients of the asymmetric prototype filter p.sub.0 that reduce the composite objective function e.sub.tot.