Digital Filter Design Overview

This section (adapted from [432]), summarizes some of the more
commonly used methods for digital filter design aimed at matching a
*nonparametric*
frequency response, such as typically obtained from input/output
measurements. This problem should be distinguished from more
classical problems with their own specialized methods, such as
designing lowpass, highpass, and bandpass filters
[346,365], or peak/shelf equalizers
[561,452], and other utility filters designed from
*a priori* mathematical specifications.

The problem of fitting a digital filter to a prescribed frequency response may be formulated as follows. To simplify, we set .

Given a continuous complex function
,
corresponding to a causal desired frequency response,^{9.8} find a stable digital filter of the form

where

(9.15) | |||

(9.16) |

with
given, such that some *norm* of the error

is minimum with respect to the filter coefficients

The filter coefficients are constrained to lie in some subset , where . The filter coefficients may also be complex, in which case .

The approximate filter
is typically *constrained* to be
stable, and since
is causal (no positive powers of
),
stability implies causality. Consequently, the impulse response of the
model
is zero for
.

The filter-design problem is then to find a (strictly) stable
-pole,
-zero digital filter which minimizes some norm of
the error in the frequency-response. This is fundamentally
*rational approximation of a complex function of a real
(frequency) variable, with constraints on the poles*.

While the filter-design problem has been formulated quite naturally,
it is difficult to solve in practice. The strict stability assumption
yields a compact space of filter coefficients
, leading to the
conclusion that a best approximation
*exists* over this
domain.^{9.9}Unfortunately, the norm of the error
typically is
not a *convex*^{9.10}function of the filter coefficients on
. This
means that algorithms based on gradient descent may fail to find an
optimum filter due to their premature termination at a suboptimal
local minimum of
.

Fortunately, there is at least one norm whose global minimization may
be accomplished in a straightforward fashion without need for initial
guesses or ad hoc modifications of the complex (phase-sensitive) IIR
filter-design problem--the *Hankel norm*
[156,432,178,36]. Hankel norm methods for digital
filter design deliver a spontaneously *stable* filter of any
desired order without imposing coefficient constraints in the
algorithm.

An alternative to Hankel-norm approximation is to reformulate the
problem, replacing Eq.
(8.17) with a modified error criterion so that
the resulting problem can be solved by *linear least-squares* or
*convex optimization* techniques. Examples include

*Pseudo-norm*minimization: (Pseudo-norms can be zero for nonzero functions.) For example, Padé approximation falls in this category. In Padé approximation, the first samples of the impulse-response of are matched exactly, and the error in the remaining impulse-response samples is ignored.*Ratio Error*: Minimize subject to . Minimizing the norm of the ratio error yields the class of methods known as*linear prediction*techniques [20,298,299]. Since, by the definition of a norm, we have , it follows that ; therefore,*ratio error methods ignore the phase of the approximation*. It is also evident that ratio error is minimized by making larger than .^{9.11}For this reason, ratio-error methods are considered most appropriate for modeling the*spectral envelope*of . It is well known that these methods are fast and exceedingly robust in practice, and this explains in part why they are used almost exclusively in some data-intensive applications such as speech modeling and other spectral-envelope applications. In some applications, such as adaptive control or forecasting, the fact that linear prediction error is minimized can justify their choice.*Equation error*: MinimizeThe above expression makes it clear that equation-error can be seen as a frequency-response error weighted by . Thus, relatively large errors can be expected where the poles of the optimum approximation (roots of ) approach the unit circle . While this may make the frequency-domain formulation seem ill-posed, in the time-domain,

*linear prediction error*is minimized in the sense, and in certain applications this is ideal. (Equation-error methods thus provide a natural extension of ratio-error methods to include zeros.) Using so-called*Steiglitz-McBride iterations*[289,452,290], the equation-error solution iteratively approaches the norm-minimizing solution of Eq. (8.17) for the L2 norm.Examples of minimizing equation error using the matlab function

`invfreqz`are given in §8.6.3 and §8.6.4 below. See [452, Appendix I] (based on [432, pp. 48-50]) for a discussion of equation-error IIR filter design and a derivation of a fast equation-error method based on the Fast Fourier Transform (FFT) (used in`invfreqz`).- Conversion to
*real-valued approximation*: For example,*power spectrum matching*, i.e., minimization of , is possible using the Chebyshev or norm [432]. Similarly,*linear-phase*filter design can be carried out with some guarantees, since again the problem reduces to real-valued approximation on the unit circle. The essence of these methods is that the*phase-response*is eliminated from the error measure, as in the norm of the ratio error, in order to convert a complex approximation problem into a real one. Real rational approximation of a continuous curve appears to be solved in principle only under the norm [376,377]. *Decoupling poles and zeros*: An effective example of this approach is Kopec's method [432] which consists of using ratio error to find the poles, computing the error spectrum , inverting it, and fitting poles again (to ). There is a wide variety of methods which first fit poles and then zeros. None of these methods produce optimum filters, however, in any normal sense.

In addition to these modifications, sometimes it is necessary to
reformulate the problem in order to achieve a different goal. For
example, in some audio applications, it is desirable to minimize the
*log-magnitude* frequency-response error. This is due to the way
we hear spectral distortions in many circumstances. A technique which
accomplishes this objective to the first order in the
norm is
described in [432].

Sometimes the most important spectral structure is confined to an
interval of the frequency domain. A question arises as to how this
structure can be accurately modeled while obtaining a cruder fit
elsewhere. The usual technique is a weighting function versus
frequency. An alternative, however, is to frequency-warp the problem
using a first-order *conformal map*. It turns out a first-order
conformal map can be made to approximate very well
frequency-resolution scales of human hearing such as the *Bark
scale* or *ERB scale* [461]. Frequency-warping
is especially valuable for providing an effective weighting-function
for filter-design methods, such as the Hankel-norm method,
which have no mechanism for imposing an explicit weighting-function on
the frequency-response error.

There are several methods which produce
instead of
directly. A *fast spectral factorization* technique is
useful in conjunction with methods of this category [432].
Roughly speaking, a size
polynomial factorization is replaced
by an FFT and a size
system of linear equations.

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