People who work in the audio industry use all kinds of different measurements to evaluate the performance of equipment. In many cases, the measurements we do are chosen because they’re easy to do (or because they were easy to do in “The Old Days”), and not because they accurately represent how the equipment actually behaves.

Magnitude response

One simple example of this is what most people call a frequency response but what is actually a magnitude response. This is a measure of how the level of an audio signal is changed by the device under test (the “DUT”) as a function of frequency. For example, if you’re measuring a RIAA-spec preamplifier (used for converting a turntable’s pickup’s output to a “line” level signal), then it should have a magnitude response that looks like the red line in the plot in Figure 1.

This curve shows that, relative to a signal at 1 kHz, the lower the frequency, the more gain is applied to the signal and the higher the frequency, the more attenuation is applied to the signal. Note that this curve is normalised to the level at 1 kHz, which should actually be +40 dB higher if we were to include the frequency-independent gain of the system.

It’s important to remember that this plot shows us only one thing: the change in level caused by the DUT as a function of a change in frequency of the signal. What this plot does NOT show us is much, much more… For example:

• We don’t know anything about the behaviour of the system outside the boundaries of this plot.
• We don’t know anything about its phase response.
• We don’t know anything about how loud the noise of the DUT is.
• We don’t know if this plot is true if we were to measure the DUT at a different input level.
• We don’t know whether the DUT would have a different behaviour if the device that was feeding it had a different output impedance.
• We don’t know whether the DUT would have a different behaviour if the device that it was feeding had a different input impedance.
• We don’t know anything about whether the signal has any non-linear distortion artefacts.
  (Notice that I didn’t say “…whether the signal is distorted” because we know it’s distorted, since the output of the DUT is not the same as the input of the DUT. Any change in the signal is a form of distortion of the signal.)

I’m not saying that a simple magnitude response plot of a DUT is not useful. I’m just saying that it’s not enough information. It’s like asking for the temperature of a cup of coffee. It’s useful information, but it doesn’t tell you enough to know whether you’re going to enjoy drinking it (unless, of course, you hate coffee…)

This problem gets even worse when you’re measuring the acoustic output of a device like a loudspeaker or a pair of headphones, for example. (The acoustic input of a microphone is a similar problem in the opposite direction.)

Let’s start by thinking about a loudspeaker’s output in real life.

1. You have a device that radiates sound in space in all directions. Let’s look at that space from the loudspeaker’s perspective and say that this means an angle of rotation around the loudspeaker, and an angle of elevation above/below the loudspeaker. That makes two dimensions.
2. If we’re talking about the loudspeaker’s magnitude response, then we’re looking at its output level (one dimension) as a function of frequency (one more dimension).
3. That speaker is (usually) in a room, and you’re probably also there too. We can then that this is in three-dimensional space when we talk about the walls, floor, ceiling, and your location inside that space.
4. Since the surfaces in the room reflect the audio signal, then the time at which the signal arrives at the listening position must also be considered. The “sound” of a loudspeaker at a listening position before the first reflection arrives is different than after a bunch of reflections are coming in and the room has started resonating as well. So, time adds one more dimension to the problem.
5. We’ll ignore the non-linear distortion artefacts produced by the loudspeaker and the fact that they radiate in different directions differently, since it’s already complicated enough… However, if we were to add things like changes in the response due to temperature of the voice coil or directionally-dependent distortion artefacts like breakup, this would wind up being a much longer discussion…

So, just looking at the small list of “usual suspects” above, we can see that evaluating the sound of a single loudspeaker in a listening room is at least an 8-dimensional problem. And this doesn’t even take things like 2-channel stereo or 7.1.4 multichannel or whether you’re listening to Aretha Franklin or Stockhausen into account…

In other words, it’s complicated. So, we use reductionism to try to start to get an idea of what’s going on. We put a microphone directly in front of a loudspeaker and measure its magnitude response at one level using one kind of test signal (e.g. a swept sine wave or an MLS) and we remove all the room’s reflections somehow. This reduces our 8-dimensional problem to a 2-dimensional version: we have level as a function of frequency and nothing else, since we’ve chosen to throw away everything else by the way we did the measurement.

For example, take a look at the magnitude response shown in Figure 2, which is a real measurement of a real loudspeaker. This measurement was performed using a swept-sine (a sinusoidal wave with a frequency that changes smoothly over time, typically from low to high) with a microphone on-axis to the loudspeaker at a distance of 3 m. The measurement was time-windowed to remove the room reflections, and therefore can be considered to be a “free field” (a sound field that is free of reflections) measurement. However, the roll-off in the low end is actually a combination of the actual response of the loudspeaker and the artefacts of using a shorter time window. (We would have needed to use a much bigger room to get less influence from the time windowing.)

So, this plot ONLY tells us how the loudspeaker behaves at one point in infinite space, when we’re ONLY asking “how does the level of the loudspeaker’s output vary with changes in frequency and we ONLY play sinusoidal signals at one level.” This is all useful information, but we need to know more – otherwise, we’ll jump to conclusions about whether this loudspeaker sounds “good” or not.

Just like looking at ONLY the temperature of a cup of coffee, this doesn’t give us enough of the story to know how the loudspeaker will “sound” (no matter what a magazine reviewer will try and tell you…).

In other words, if we use reductionism to understand the problem, you simplify the question so much that the problem you wind up understanding is not the same as the thing you’re trying to understand in the first place.

For example, if we measure that same loudspeaker at a different angle (by rotating the loudspeaker and leaving the microphone in place) we’ll see a magnitude response like the one shown in Figure 3.

This magnitude response is the output of the same loudspeaker at 90º off-axis, which might be what’s heading towards your side-wall. If your side wall is perfectly reflective, then this is therefore the magnitude response of your first reflection, which might be a bad thing if you think that it’s important.

So, when you’re looking at any one measurement of anything, you don’t have enough information to know enough to make a general evaluation. However, unfortunately, many people will run with this information and make the evaluation anyway. It’s data, and data doesn’t lie, so this tells the truth, right?

Wrong. Because it’s only a portion of the total truth.

For example, you can say that “organic food is good for me” but I have an allergy to peanuts. So if I eat organic peanuts, I have about 20 minutes to get to a hospital. Much longer than that and I need a funeral home instead. “Organic” is true, but not enough information for me to know whether or not it’ll be an uneventful meal.