Audio signal dither and noise shaping
i dunno what a "Lipshitz filter" is but i do know who Stanley Lipshitz is.  now, i don't know for sure what Stanley would say to you, but the best advice i got from him is that it does little good to color the additive dither if you are also using error feedback.  if there is no noise shaping feedback, probably the simplest way to get triangular PDF dither is to generate good, independent, rectangular PDF random numbers $v[n]$ and run that through a differentiator.

$$ d[n] = v[n] - v[n-1] $$

the result is high-pass in the spectrum and triangular PDF.  if $\Delta$ is the step size of the quantization, $\frac{\Delta^2}{12}$ is the variance (or power) of raw quantizer, but you have two more of them due to your dither and $\frac{\Delta^2}{4}$ is the total power due to the entire dithered quantizer.  a 4.77 dB increase in quantization error, but the quantization is fully decoupled from both the mean and variance of the input signal.  although this is the same quantization power that you would get from adding two independent random numbers, i think you would prefer that much of the power is steered to higher frequencies (but not all of it, the spectrum of the raw quantizer is still flat).

if you're planning on error feedback, i think the best advice is either use white, triangular PDF dither (requires adding two independently generated rectangular PDF random numbers) and error feedback (designing a good feedback filter takes a bit).

or no dither (if the quantization step size is very small) and error feedback.  the simplest no-trivial example of this is "fraction saving" where you simply hack off the low-order bits (the floor() function) and add those bits back in (zero-extended on the left) to the next sample before that sample is quantized (with the floor() function).  it has infinite S/N ratio at DC and totally kills any limit cycle in feedback filters (like biquads) that get the filter stuck on a DC value even when the input goes to zero.  i've done that a lot.