Practical Advice

To summarize this appendix, the following pointers can be offered:

• Verify that aliasing can be heard and sounds bad before working to get rid of it.

• Aliasing (bandwidth expansion) is reduced by smoothing ``corners'' in the nonlinearity.

• Consider using an oversampling factor for nonlinear subsystems that is sufficient to accommodate the bandwidth expansion caused by the nonlinearity.

• Make sure there is adequate lowpass filtering in a feedback loop containing a nonlinearity.

As a specific example, consider the cubic nonlinearity used in a feedback loop (as in §4.13). This can be done with no aliasing at low levels (i.e., at levels below hard clipping) provided we use

• $3\times$ oversampling, and
• a lowpass filter to $\omega\in[-\pi/3,\pi/3]$ after the nonlinearity.

To avoid $3\times$ oversampling in the entire feedback loop, we may downsample by 3 after lowpass filter and upsample by 3 just before nonlinearity. If the lowpass filter is good, the downsampling by 3 is trivially accomplished by throwing away every 2 out of 3 samples. For upsampling, however, an additional third-band lowpass-filter is needed for the interpolation (see §J.3).

Another variation is to oversample by two, in which case there is aliasing, but that aliasing does not reach the ``base band.'' Therefore, a half-band lowpass filter rejects both the second spectral image and the third, which is aliased onto the second.