Now, before you start sending me hate mail because you think this posting is a Windows vs. Mac lecture, hold your horses. That’s NOT the kind of windows I’m talking about. This one’s about windowing functions and one (possibly unexpected) effect on the results of the analysis of the impulse response of an allpass filter. So, if you want to debate Windows vs. Mac – go somewhere else. If you think that you can get all riled up over a Blackman Harris window function, read on!

Last week I had to do some frequency-domain analysis of a system that had a small problem with noise in its impulse response measurements. The details of where the noise came from are unimportant. There is only one important thing from the back-story that you need to know – and that is that I was measuring the response of an allpass filter implementation.

So, I did my MLS measurement of the allpass filter and, because I had noise in the impulse response, I chose to use a windowing function to clean up the impulse response’s tail. Now, I know that, by using a windowing function (or a DFT, for that matter), there are consequences that one needs to be aware of. However, the consequence that I stumbled on was a new one for me – although in retrospect, it should not have been.

Here’s a sterilised version of what happened, just in case it’s of use.

Below is a plot showing a (very clean) impulse response of an allpass filter. To be more specific, it’s a 4th order Linkwitz Riley crossover with a crossover frequency of 100 Hz, where I summed the outputs of the high pass and low pass components together to make an output. (We will not discuss why I did it this way, since that information is outside the scope of this discussion.) In addition, I have plotted three windowing functions, a Hann, a Hamming and a Blackman Harris.

Note that the length of the windowing functions is big – 65536 samples to be exact. As you can see in the plot, the ringing of the allpass filter is negligible in this plot by the time we get to the end of the window. This can also be seen below in the next two plots where I’ve shown the impulse response after it has been windowed by the three (actually four, if we include rectangular as a function), scaled in linear and dB FS. (I know, I know, dB FS is an RMS measurement and I plotted this as instantaneous values – sue me.)

So, if you now take those windowed impulse responses and calculate their magnitude and phase responses, you get the plots shown below.

“So what?” I hear you cry. The magnitude responses of the four versions of the windowed impulse response are all identical enough that their plots lie on top of each other. This is also true for their phase responses. “I see what I would expect to see – what are you complaining about?” I hear you cry.

Well, let me tell you. The plots above show the results when you use a 65536-point FFT and a 65536-sample window (okay, okay, DFT – sue me).

Let’s do all that again, but with a 65536-point FFT and a 1024-point window instead (I did this in MATLAB, so it’s zero-padding the impulse responses with the remaining 65536-1024 = 64512 samples.)

Now we can see immediately, that the ringing in the allpass filter’s impulse response hasn’t settled down by the time we get to the end of the window. This can also be seen in the following two plots.