Window Method for FIR Filter Design

The *window method* for digital filter design is fast,
convenient, and robust, but generally suboptimal. It is easily
understood in terms of the *convolution theorem* for Fourier
transforms, making it instructive to study after the Fourier theorems
and windows for spectrum analysis. It can be effectively combined
with the frequency sampling method, as we will see in §4.6
below.

The window method consists of simply ``windowing'' a theoretically
*ideal* filter impulse response
by some suitably chosen window
function
, yielding

(5.8) |

For example, as derived in Eq. (4.3), the impulse response of the

sinc | (5.9) |

where is the total normalized bandwidth of the lowpass filter in Hz (counting both negative and positive frequencies), and denotes the cut-off frequency in Hz. As noted earlier, we cannot implement this filter in practice because it is noncausal and infinitely long.

Since
**sinc**
decays away from time 0 as
, we would
expect to be able to truncate it to the interval
, for some
sufficiently large
, and obtain a pretty good FIR filter which
approximates the ideal filter. This would be an example of using the
window method with the *rectangular* window. We saw in
§4.3 that such a choice is optimal in the least-squares
sense, but it designs relatively poor audio filters. Choosing other
windows corresponds to *tapering* the ideal impulse response to
zero instead of truncating it. Tapering better preserves the shape of
the desired frequency response, as we will see. By choosing the
window carefully, we can manage various trade-offs so as to maximize
the filter-design quality in a given application.

Window functions are always *time limited*. This means there is
always a finite integer
such that
for all
. The final windowed impulse response
is thus always time-limited, as needed for practical
implementation. The window method always designs a
*finite-impulse-response* (FIR) digital filter (as opposed to an
*infinite-impulse-response* (IIR) digital filter).

By the dual of the convolution theorem, pointwise multiplication in the time domain corresponds to convolution in the frequency domain. Thus, the designed filter has a frequency response given by

(5.10) |

where is the ideal frequency response and is the window transform. For the ideal lowpass filter, is a rectangular window in the frequency domain. The frequency response is thus obtained by convolving the rectangular window with the window transform . This implies several points which can be immediately seen in terms of this convolution operation:

- The
*pass-band gain*is primarily the area under the*main lobe*of the window transform, provided the main lobe ``fits'' inside the pass-band (*i.e.*, the total lowpass bandwidth is greater than or equal to the main-lobe width of ). - The
*stop-band gain*is given by an integral over a portion of the*side lobes*of the window transform. Since side-lobes oscillate about zero, a finite integral over them is normally much smaller than the side-lobes themselves, due to adjacent side-lobe cancellation under the integral. - The best stop-band performance occurs when the cut-off
frequency is set so that the stop-band side-lobe integral traverses a
whole number of side lobes.
- The
*transition bandwidth*is equal to the bandwidth of the*main lobe*of the window transform, again provided that the main lobe ``fits'' inside the pass-band. - For very small lowpass bandwidths
,
approaches an
*impulse*in the frequency domain. Since the impulse is the identity operator under convolution, the resulting lowpass filter approaches the window transform for small . In particular, the stop-band gain approaches the window side-lobe level, and the transition width approaches*half*the main-lobe width. Thus, for good results, the lowpass cut-off frequency should be set no lower than half the window's main-lobe width.

[How to cite this work] [Order a printed hardcopy] [Comment on this page via email]

[Lecture Video] [Exercises] [Examination]

Copyright ©

Center for Computer Research in Music and Acoustics (CCRMA), Stanford University