FIR Digital Filter Design
Optimal (but poor if unweighted)
Least-Squares
Impulse Response Design
ExamplesSearch Spectral Audio Signal Processing
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Figure E.2 shows the amplitude response of a length
optimal least-squares FIR lowpass filter, for the case in which the
cut-off frequency is one-fourth the sampling rate (
).
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We see that, although the impulse response is optimal in the least
squares sense (in fact optimal under any
norm with any
error-weighting), the filter is quite poor from an audio
perspective. In particular, the stopband gain, in which zero is
desired, is only about 10 dB down. Furthermore, increasing the length
of the filter does not help, as evidenced by the length 71 result in
Fig.E.3.
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It is not the case that a length
FIR filter is too short for
implementing a reasonable audio lowpass filters, as can be seen in
Fig.E.4. The optimal Chebyshev lowpass filter in
this figure was designed by the matlab statement
hh = remez(L-1,[0 0.5 0.6 1],[1 1 0 0]);We see that the Chebyshev design has a stopband attenuation better than 60 dB, no corner-frequency resonance, and the error is ``equiripple'' in both stopband (visible) and passband (not visible). Note also that there is a transition band between the passband and stopband (specified in the call to remez as being between normalized frequencies 0.5 and 0.6).
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The main problem with the least-squares design examples above is the absence of a transition band specification. That is, the desired filter specification asks for infinite roll-off rate, which is too much. (See Fig.E.25 for an illustration of more practical lowpass-filter design specifications.) With a transition band and a weighting function, least-squares FIR filter design can perform very well in practice.
