The Right Call: How To Avoid Poor Equalization Choices
you don't know the answer before you start to measure, how do you know you are
getting a good measurement?"
If I were to ask you to measure the voltage coming out of the electrical outlet closest to you using a multimeter or VOM (volt-ohm-millimeter), you would have expectations. However, should the multimeter's display, for whatever reason, not show the expected voltage for your specific region, there's a valid reason to start investigating. Maybe the meter's batteries are dead or maybe a circuit breaker tripped. Regardless, you were right to question the outcome because it didn't meet expectations.
By extension, one can argue that the same can be said for using a dual-channel FFT analyzer, except that most users have difficulties predicting what the results are supposed to look like and are tempted to accept the outcome at face value with little to no scrutiny. Complicating matters further: how the analyzer is set up will greatly affect the appearance of the results, which is the focus of this article.
Loudspeakers with a flat (or otherwise desirable) free field frequency response become "unequalized" upon deployment (typically as part of a larger sound system) for reasons beyond the scope of this discussion. However, those who use analyzers resort to their computer screens to identify the changes the loudspeaker (or sound system) have undergone in order to potentially "equalize" those changes where applicable.
Today's FFT (fast Fourier transform) analyzers provide so much resolution (especially compared to real time analyzers – RTA – with only third-octave resolution) that users, out of the gate, typically resort to gratuitous amounts of smoothing to even out the responses in an attempt to make sense out of the madness.
However, all that detail such as ripple, prior to smoothing, is not necessarily bad. Did you know that loudspeakers are expected to exhibit ripple, even under "ideal" circumstances such as an anechoic room (Figure 1)?
If I were to show you the edge of a razor blade under an electron microscope, you'll likely never shave yourself again even though it's a perfectly good razor blade that's "razor-sharp." As in medicine, we should apply the principal precept of "first, do no harm" because not every detail we see on an analyzer justifies intervention (Figure 2).
We know this isn't true unless we invoke a change such as actual EQ. So what could have led these audio professionals to conclude that transfer functions change with audience enthusiasm (or lack thereof), which would suggest a different sounding system (psycho-acoustic phenomena such as masking excluded)? It's an important question in the interest of eye-to-ear training if we want to measure what we hear and hear what we measure.
During a live concert, audience noise (among other things) can alter the appearance of a transfer function depending on how the analyzer is set up. Therefore, it's in our best interest to understand these settings and how they affect the transfer function's appearance so we can deliberately refrain from using EQ (do no harm), because audience noise doesn't "unequalize" a sound system.
When repeated enough times you'll be able to ultimately reconstruct the complete sentence and the message is finally received. As long as the message stays consistent with each repetition, enough of them should ultimately allow you to overcome the background noise.
Analyzers, when set up correctly, offer similar functionality, where each doubling of the number of averages translates into a 3 dB boost in SNR without actuality cranking up the excitation signal level by brute force. Each time the number of averages is doubled, twice the amount of correlated data (the excitation signal) is captured, making signal gain 6 dB more market share.
Irrevocably, each time the number of averages is doubled, also twice the amount of contaminating uncorrelated data (noise) is captured. However, doubling uncorrelated data (unlike correlated signals) only results in a 3 dB increase. Therefore, the net increase in SNR equals the 6 dB signal boost minus the 3 dB noise boost, leaving 3 dB in favor of signal-over-noise for each doubling of averages.
Increasing the number of averages can artificially suppress the noise (floor) without raising the excitation signal level with brute force, provided you choose the correct type of magnitude averaging. Magnitude averaging can be performed in two vastly different ways (or types) called RMS and vector averaging, which can (and are likely to) affect the transfer function's appearance to a great extent.
"Whether you owe me money or I owe you money,
When dealing with scalars, there's no direction that makes RMS averaging time blind, as we're about to discover.
However, our excitation signal, when represented by a vector, preserves direction as well as magnitude over time. When averaged, the mean magnitude and direction are expected to be identical to the average's constituent components (our excitation signal). Notice that in Figure 4, both magnitude and direction for signal-plus-noise approach those of signal, which is what we're ultimately after, as SNR or the number of averages is increased.
As with our hearing sense and brain in the crowded bar, vector averaging features the ability to progressively reject noise with increasing averaging. Further, having a direction descriptor in addition to magnitude (unlike the scalar), makes vectors subject to phase angles and inherently time.
When do RMS and vector averaging produce identical transfer functions? Only when:
1) Measurement and reference signals are properly synchronized (delay locator).
2) There's little to no contamination by non-coherent signals, i.e., ample SNR and D/R.
Figure 5 shows that as long as there's ample SNR, i.e., 10 dB or more, as seen at 125 Hz and below, RMS- and vector-averaged transfer functions are in good agreement. But, with 0 dB of SNR or less, as seen at 1 kHz and above, noise determines the appearance of the RMS-averaged transfer function (blue) and no amount of averaging will change that.
Are you convinced that an audience is always 10 dB less loud than the sound system during songs? Notice that the vector-averaged transfer function (green), even for negative signal-to-noise ratios (SNR less than zero), remains virtually unaffected with help of a modest amount of averaging.
In the absence of noise, both RMS- (blue) and vector-averaged (green) transfer functions are indeed identical. When SNR is reduced to 20 dB (black line spectrum in RTA plot), with respect to the signal's spectral peaks (pink line spectrum in the RTA plot), appearances change. Notice that the cancels (valleys) of the RMS-averaged transfer function (blue) rose which is also true for the vector-averaged transfer function (green) but to much lesser extent as you can tell from looking at the orange trace underneath which is the original comb filter in the absence of noise. The transfer function peaks for both types of averaging are in good agreement.
When SNR is reduced to 10 dB, the blue valleys rise substantially, which reveals a serious weakness of RMS averaging which is that its incapable of distinguishing noise from signal. Signal (pink) has been destroyed at certain frequencies due to destructive interaction and all that's left at those frequencies is residual background noise (black) that "fills" these cancels (valleys).
Regardless, this uncorrelated contamination makes the RMS-averaged transfer function look different. The vector-averaged transfer function (green), with help of a slight increase in averaging, remains in much better agreement with the orange reference trace. The transfer function peaks for both types of averaging are still in good agreement with 10 dB of SNR.
When SNR is reduced to 5 dB, the frequency response ripple for the RMS-averaged transfer function (blue) is about 6 dB. It appears as if we're in an anechoic room (refer back to Figure 1), whereas the vector-averaged transfer-function (green) persists and shows us the real deal; that is to say, the actual degree of interaction which we can tell from the green ripple that has not been obfuscated by noise.
Also, with this little SNR (5 dB), the transfer function peaks for both types of averaging are no longer in good agreement. For all frequencies, the RMS-averaged transfer function (blue) rose which is the limitation of RMS averaging where signs have been lost. As SNR is reduced, RMS-averaged transfer functions can only go up.
When SNR is reduced to 0 dB, both transfer functions look completely different. The vector-averaged transfer function (green) persists and continues to show how severely compromised the system is (strong ripple) unlike the RMS-averaged transfer function (blue) where the first cancel (valley) that is 1-octave wide (11 percent of the audible spectrum) has been filled to the top with noise (which also goes for the remaining cancels).
Finally, when SNR is reduced to -6 dB and noise is now actually louder than signal, the blue RMS-averaged transfer function suggests there's no (destructive) interaction whatsoever, whereas the green vector-averaged transfer function persists. Under such extreme conditions, one can even set the averager to infinity (accumulate) in which case it continues to average for as long as the measurement is running, improving SNR artificially as time passes by. How long should we average? Until there's no apparent change in the data? Why wait longer without getting anything in return?
By now, I hope you can appreciate how (background) noise can really mess with the appearance of transfer functions depending on how the analyzer is set up, and EQ decisions should be made with scrutiny. However, noise is just one subset of a larger family of non-coherent signals.
Real-world measurements of loudspeakers and sound systems will be contaminated with non-coherent signals; that is to say, uncorrelated signals that are not caused exclusively by a system's input signal (causality), or correlated signals that are no longer linearly dependent on the system's input signal. Non-coherent signals come in many flavors such as noise (like our audience), late arriving energy (reverberation) outside the analysis window and distortion.
& Detrimental Reverberation
It would be extremely ill-advised to make EQ decisions based on echo-contaminated measurements since echoes don't "un-equalize" the loudspeaker or sound system. Echoes are late arriving discrete copies of sounds, that originated at a source, which have become "un-fused" from the direct sound (first arrival).
If you're dealing with echoes, re-aim the loudspeakers and work to keep their sound away from specular surfaces that reflect, which causes the echoes (prevention). The other option is to absorb the sonic energy upon impact when re-aiming is not an option (symptom treatment). EQ should only be used as a last resort when all other options have been exhausted, because EQ can't differentiate between the very thing we're trying to preserve: the mix, and the thing we're trying to prevent: the echo (first, do no harm). If analyzers could somehow reject echoes, it would help prevent making poor EQ choices.
The finite linear impulse response (think of an oscilloscope) shown in the video in the upper plot of what is Figure 6 is also known as the "analysis window" whose duration is determined by the FFT-size and sample rate. In this example, the FFT-size is 16K (214 = 16384 samples) which at a sample rate of 96 kHz translates to a 171-milisecond-long window.
Signals that arrive in the center of the analysis window, while the "door" is ajar, are properly synchronized and arrive in time whereas signals which arrive outside the analysis window are out of time. How will this affect the transfer functions depending on the magnitude averaging type?
The RMS-averaged transfer function (blue) continues to look the same, even when the measurement signal arrives outside the analysis window, like echoes are known to do. This implies that RMS averaging can't tell direct sound from indirect sound and is time blind! Like noise, reverberant energy can only add to the RMS-averaged transfer function (blue) and make it rise whether it's on time or not.
Remarkably, the vector-averaged transfer function drops proportionally as the measurement signal moves towards the edge of the analysis window. By the time the measurement signal arrives outside the analysis window (late by 85 ms or more) it has been attenuated by at least 10 dB for the current number of averages. Increasing the number of (vector) averages will attenuate the signal even further. It's like "virtual" absorption has been applied which attenuates late arriving signals such as echoes (unlike RMS averaging, which is time blind).
So how late does a signal have to be to constitute an echo?
A conservative estimate but more realistic delay (again, for reasons beyond the scope of this article) is 24 cycles, which is also frequency dependent. Twenty-four cycles equal 240 ms at 100 Hz, 24 ms at 1 kHz, and only 2.4 ms at 10 kHz! It's clearly not the same time for all frequencies. When we pursue the idea of a sole time delay for all frequencies, we're doing a tap delay – which is a "rhythmic" echo – whereas real echoes are two or more discrete instances of the same signal that have become un-fused, and where the time gap is frequency dependent.
So, while vector-averaging is capable of attenuating echoes which arrive outside the analysis window, clearly a single fixed analysis (time) window won't suffice.
(Analysis) Time Windows
This brings us to the second important advantage of multiple windows, which is that high-frequency echoes will be rejected much sooner than low-frequency echoes, provided we resort to vector-averaging and properly set the delay locator. This gives vector-averaging the capability to capture and preserve useful reverberation such as (stable) early reflections that strongly affect tonality, and arrive inside the analysis time windows, while rejecting detrimental reverberation such as echoes which arrive outside the analysis time windows. To RMS averaging it's all the same...
It would be really convenient if there was one more metric that could increase our confidence and keep us from making poor EQ decisions.
In the absence of non-coherent signals, coherence is expected to have a value of 1, or 100 percent. In the absence of coherent signals, coherence is expected to have a value of 0, or 0 percent. It's a "lump" indicator because non-coherent signals come in many flavors.
Since coherence is always computed vectorially, it's susceptible to phenomena such as noise, late-arriving energy outside the analysis windows (echoes), and even distortion. What it won't do is tell us which is which. For that, we need an analyzer that's not the software. The software is just a tool, like an army knife. We users are the actual analyzers (thank you Jamie Anderson) and it's up to us to determine the nature of non-coherent power...
Putting It Together
If we look at Equation 1, we should be able to appreciate that one part coherent power and one part non-coherent power equals half (or 50 percent) coherence, or a 0 dB coherent-to-non-coherent power ratio. In other words, 50 percent is the break-even point which is indicated with the black dashed line in both plots.
RMS- and vector-averaged transfer functions are expected to be near identical when coherent power exceeds non-coherent power by 10 dB, independent of the flavor of non-coherent power. Since coherence deals with signal power, you should apply the 10log10 rule that informs us that +10 dB equals a factor x10. Therefore, 10 parts coherent power against one-part non-coherent power equal 10/11 (Equation 1) or 91 percent coherence.
When coherence equals 91 percent or more, notice that both transfer functions in the upper plot are in good agreement. However, once that condition is no longer met, the traces start to differ substantially. By how much exactly can be viewed in the bottom plot, where we see the difference with help of "Quick Compare."
Vector averaging will always give us an objective measurement, even under hostile circumstances, provided we use enough averages. Under stable conditions (no redecorating the venue with a wrecking ball or moving loudspeakers), it will reveal at one point in space (where the measurement microphone lives), if, and to what degree, the sound system has become compromised due to loudspeaker-to-room and loudspeaker-to-loudspeaker interaction – and it's quite immune to change, unlike RMS averaging.
However, when coherence drops below 50 percent, non-coherent power dominates over coherent power, any EQ decisions based on RMS-averaged transfer functions should be postponed until one is convinced of the "flavor" of non-coherent power. Is it noise (including the audience), or late-arriving reverberant energy outside the analysis windows, or a combination of both? Because any EQ decisions based on the appearance of low-coherent RMS-averaged transfer functions whose shape is determined by dominating non-coherent power (bottom plot in Figure 7) – and not signal – should be made with utmost caution!
Noise can be ruled out by first capturing a line spectrum of the noise floor prior to turning on the excitation signal (generator). Once the noise floor has been captured, we can turn on the generator and capture another line spectrum. If the latter lives 10 dB or more above the former, it should be good. This could still leave local SNR-minima at certain frequencies where the direct sound has been killed due to destructive interaction, which is something that vector-averaged transfer functions and coherence reveal.
Evidently this option is no longer available come show time, and you'll have to make a judgment call about the signal-to-audience ratio before considering changing any EQ – that is, if you're still convinced that audience sounds can "un-equalize" a sound system. (I'm kidding.)
If actual noise has successfully been ruled out for low-coherent RMS-averaged data, the remaining culprit is most likely late-arriving detrimental reverberant energy, which implies that we're facing a negative direct-to-reverberant ratio (D/R) that EQ can't fix. This is because EQ doesn't alter the directional behavior of a loudspeaker or sound system, nor does it add absorption to the venue, nor does it close the distance between audience and sound system. The remaining options speak for themselves.
While first arrivals are always anechoic, indoors, reverberation is destined to catch up and stable (early) first order reflections are likely to add gain (room gain) that greatly affect the listening experience. This kind of useful reverberation is admitted for both RMS- and vector-averaged transfer functions, but in addition, the latter averaging type rejects detrimental reverberation, which requires a different remedy.
But even more important is a proper understanding of coherence, because it will inform us whether we're measuring the sound system or the audience. Regardless, high-coherent data is reliable, actionable data.
About Merlijn van Veen