Back when I was at McGill, one of my fellow Ph.D. students was Mark Ballora, who did his doctorate in converting heart rate data to an audible signal that helped doctors to easily diagnose patients suffering from sleep apnea.

I found this at a flea market yesterday and I couldn’t resist buying it. It’s a Sharp EL-805M “pocket” calculator that was released for sale in 1973 and discontinued in 1974.

This would have been a time when a Liquid Crystal display was a feature worth advertising on the front panel of the calculator (since this was the first calculator with an LCD).

Sharp was one of the pioneers of calculators using the DSM (Dynamic Scattering Mode) LCD (Liquid Crystal Display). These DSM LCDs have the now unusual feature of silver-like reflective digits on a dark background, rather than the now common black digits on a light background.

It was also from a time when instructions were included on how to use it. Notice the instructions for calculating 25 x 36, for example…

Undoubtably, the best 20 DKK I spent all weekend, given that the original price in 1973 was 110 USD.

For a peek inside, this site has some good shots, but it seems that it proves to be a challenge for automatic translators. There’s also a good history here.

N.B. I updated this page on 2023 04 05 based on new information from our suppliers…

We have two cars. One is a fully-electric car, and the other is a diesel.

Originally, the plan we had with our electricity supplier for the electric car was a flat fee per month, and an “all you can eat” plan. This made the choice of which car to drive a no-brainer: take the electric car whenever possible.

However, due to the rising price of energy, our supplier is changing their plan to a new pricing structure. The new price will be

The reasoning behind this pricing is explained on their website – I won’t bother getting into that.

Note that they define the “average electrical price” as the average monthly price for both DK1 and DK2 (Denmark is split into two regions for electricity prices). The calculation is done on a charge-by-charge basis, where the month that’s chosen for the calculation is the month when you unplug the cable at the end of charging your car.

Our problem is that it made the decision of which car to drive (looking at it from a purely economic point of view) complicated. If we park the electric car, it still costs us 799 DKK / month + the price of diesel in the other car. On the other hand, if we drive the electric car, it costs us something that’s difficult to calculate when you’re heading out to the car in the morning with only one cup of coffee in you…

One thing that makes it even more complicated is the fact that, if we charge the electric car at home, we first pay our normal electricity supplier for the power we used, and we then get reimbursed by the electricity supplier for the electric car by some amount per kWh.

The way the electricity supplier for the electric car calculates this reimbursement is also complicated: They use the average monthly electricity price between 11:00 p.m. and 6:00 a.m. including charges. That number changes but it’s currently defaulting to 1.33 DKK / kWh on this page – look for the “Tilbagebetalingssats” amount in the sidebar on the right called “Tilbagebetaling”. (Note that this value is difficult if not impossible to determine using the NordPool information. The webpage linked above calculates it from the “forventet indkøbspris” that you can change yourself on their calculator.

It turned out that figuring out this problem was the most interesting math that I did this week. I ran the calculations first in Matlab, and then duplicated them in Excel (for compatibility’s sake) to find out how to deal with this.

The variables are:

Electrical supplier for the electric car:

Flat monthly rate for our subscription

The amount that they subtract from the average Danish price, per kWh for charging the car (currently 0.89 DKK)

The amount that they pay us back to cover a portion of the electrical costs when we charge the car at home

The price we pay for electricity for the house

Average electricity price in DKK / MWh (available from this page. Select the DK1 and DK2 prices for the month of interest. The Excel spreadsheet finds the average of those two values, and adds 25% tax. shown at the bottom in cell B17 in DKK/kWh)

Fossil fuel Price in DKK/litre (in my case, that’s diesel)

Consumption of the two cars

Average consumption of the electric car in kWh/100 km

Average consumption of the fossil-fueled car in litres/100 km

Total number of km driven per month

The result is two plots:

The one on the left shows the price of driving each car individually, based on the total number of km driven in the month, as a function of how many of those km are driven in the electric car.

The green line shows the cost of driving the electric car if we charge it at a station away from home

The red line shows the cost of driving the electric car if we charge it at home

The black line shows the cost of driving the fossil-fuel car

The one on the right shows our total price, as a function of how many of the total number of km driven are driven in the electric car.

So, as you can see in the plots above, at the current prices, and using the average consumption values for our two cars, the more we drive the electric car, the more money we save, and we’ll save a lot more money if we don’t charge at home.

Looking at the plot on the right, if we park the electric car (0 km on the X-axis) we’ll spend about 2700 DKK per month. If we only drive the electric car (2000 km on the X-axis) and charge away from home at charging stations, then we’ll spend less than 1000 DKK (green line on the right-hand plot). Quite a savings! If we charge at home, we’ll spend about 2200 DKK (red line on the right-hand plot) – still cheaper than the diesel, but more than double the price of NOT charging at home.

In case you are in the same position as we are, and the little Excel calculator I made might be useful, you can download it here. However, I make no promises about its reliability. Don’t send me an email because I screwed up the math – fix it yourself. :-)

2023 05 19 update: We switched to “spot pricing” for the house electricity. So, this calculation has become dependent on the time of day when we charge the car. As a result, I’ve given up trying to understand it…

If you’ve read through the first four parts of this series, then you’re already at a point where you can intuitively understand what’s going on. We just have a couple of details to take care of before finishing off.

Firstly, the plots showing the zeros and poles in the figures you’ve been looking at plots of the “Z-plane” or “Complex-plane“. As I said at the start, we’re only trying to get to an intuitive understanding of these plots – so I’m not going to get into complex numbers, or even much math (apart from what you’ll see below… which isn’t very complicated, and avoids complex numbers).

When I’m developing a new DSP algorithm, I use an application called Max from cycling74.com. Figure 1 shows a screenshot from Max, where I’m using an object to calculate the biquad coefficients to make a low pass filter, as you can see. I’ve then connected the output of that object (it looks like a magnitude response) to a Z-plan representation that shows me the same thing in a different way.

You may notice that this plot has two poles, one at (0, 0.408) and the other at (0, -0.408). In fact there are two zeros there as well, but they’re situated in the same place, on “on top” of the other, at (-1, 0). This is always true for a biquad – there are always two zeros and two poles. Sometimes, they’re located in the same place, sometimes not, sometimes they’re placed symmetrically, sometimes not, depending on the filter, as we’ll see below.

Let’s look at that Z-plane representation in 3-dimensions:

So, as you would now expect, the poles pull up the edge of the circle, and the zeros (both in the same place) pull down, giving the red line the height that it has.

Now, think back to this Figure from earlier in the series:

If you therefore look at Figure 3, which is like looking at Figure 4 from the top, you’ll notice that the height of the red line (the edge of the circle is high on the left (in the low frequencies) and drops as you go to the right (the high frequencies). This is the magnitude response that’s shown on the top of Figure 1. The only difference is that it’s on a linear scale instead of a logarithmic scale, so the shape looks a little weird.

Let’s do another one:

Hopefully, now you are able to look at a Z-plane representation of a filter and think about the effect of the poles and zeros on the edge of the circle, and therefore get a rough idea of the magnitude response of the filter…

If not, I apologize for wasting your time. On the other hand, if you’re in a life-threatening situation, this knowledge probably wouldn’t help you anyway… Very few people have gotten a critical injury in a biquad accident.

How I did it

If you want to make these plots for yourself, the math is pretty simple.

Start by choosing the frequency, which will be a point on the circle. You then find the four distances from the zeros and poles to that point (I’ve indicated those distances in Figure 8 with the variables z1, z2, p1, and p2.) This can be done using the Pythagorean theorem.

To find the gain of the filter at the frequency, you divide the sum of the zeros’ distances by the sum of the poles’ distances. In other words:

(z1 + z2) / (p1 + p2)

That will give you the result as a linear value. If you then want to convert it to decibels, like I’ve done, you do a little extra math like this:

20 * log10 ( (z1 + z2) / (p1 + p2) )

That’s it! You just need to do repeat that math for each frequency that you’re interested in, and you’re done!

I wrote an intuitive explanation of aliasing in this posting and dug in a little deeper, looking at the side-effects of aliasing with audio signals specifically in this posting.

One of the more important figures in that second posting is repeated below in Figure 1.

Let’s say that we wanted to make a sine wave generator in the digital domain. This is pretty easy to do using some rather simple math, as follows:

Output(n) = sin(2 * π * Fc / Fs * n)

where Fc is the frequency of the sine wave in Hz, Fs is the sampling rate in Hz, and n is the time, expressed as a sample number.

There are no restrictions on Fc – so if you wanted to plug in a value that is higher than Fs/2 (the Nyquist frequency) then you’ll get a value. However, if you used this math to try to make a sine wave where Fc > Fs/2, then the output will be different from what you expect. This is what’s shown in Figure 1. The red curve shows the actual frequency of the output (read off the Y-axis) for an intended frequency (on the X-axis).

This problem of the difference between input and output is identical to what would happen if you rotated a wheel by some angle, and then asked someone to measure the rotation. For example, look at Figure 2.

On the left, it shows a wheel that was rotated clockwise by 90º (indicated by the red arrow). Someone measuring the rotation would say that it was rotated by 90º – a perfect match! If you rotated by 180º (the second example), the person measuring would also get the right answer. However, if you rotated by 270º (the third example, in the middle), the person measuring would (correctly) say that you rotated by 90º counterclockwise. A rotation of 360º gets you back where you started, so it would be measured as 0º. A rotation of 450º (the example on the right) would be measured as a rotation of 90º.

If we were to do this a lot, and plot the results, they’d look like Figure 3.

Now compare Figure 3 to Figure 1. Notice how they’re identical? This is important because it’s a graphic example of exactly the way frequencies “wrap” in a digital audio world. This “wrapping” is the result of the fact that a sinusoidal wave (a signal containing only one frequency) is just a 2-dimensional view of a 3-dimensional rotation (I showed this with photos of a Slinky™ in this posting.

When we normal people look at a magnitude response of a device – let’s say, a low-pass filter, we put it on a nice cartesian plot with the frequency displayed on a straight line on the X-axis and the magnitude displayed on a straight line called the Y-axis. This looks something like Figure 4.

However, this is only a portion of the truth. The truth extends further than the limits of that plot. I conveniently stopped plotting at Fs/2 (since the filter that I made is running at 48 kHz, this plot goes up to 24 kHz). I also didn’t plot anything below 20 Hz – and I certainly didn’t extend the plot below 0 Hz into the negative frequencies… (“Negative frequencies?” I hear you ask… These are the same as positive frequencies, except that 3-dimensional wheel is rotating in the opposite direction; but since we’re only looking at it on-edge from one location, we can’t tell whether it’s rotating clockwise or counter-clockwise. See this posting if you want to go further.)

Let’s try extending the plot. First, I’ll show Figure 4, but using a linear scale for the frequency instead of a logarithmic scale. This is shown in Figure 5.

If I then were to plot beyond Fs/2, then the magnitude response would be a mirrored version of the one you see in Figure 4. The same would be true if I were to plot below 0 Hz. This is shown in Figure 6.

What does this mean? It means for example that, if I had an LPCM system running at 48 kHz, and I were to digitally generate a sine tone at 48 kHz, then the result would be the same as making a “sine tone” at 0 Hz (or “DC”) because all of the samples would have the same value – neither 0 Hz nor 48 kHz would be a sinusoidal wave in a 48 kHz system. If I then, inside the same system, sent that “48 kHz sine tone” through a low-pass filter with a cutoff frequency of 1 kHz, then it would go through un-impeded (just like a 0 Hz signal would get through a low-pass filter).

Assembling the pieces

Let’s take the illustration I just showed in Figure 6, and consider it, knowing what I showed in the comparison between Figures 3 and 1.

Although we normal people show each other magnitude responses that look like the one in Figure 4, this is not the way people who make digital signal processing (DSP) software think. They see the frequency axis on a circle that goes from 0 Hz up to Fs/2 (the Nyquist frequency), and then wraps back around to 0 Hz (= Fs). This weird way of viewing the world is shown in Figure 7.

There are some very good reasons why DSP engineers think like this – one of which you already know (the wrapping and aliasing issue). There are some reasons I’m not going to talk about here (but you can read this if you’re interested), and there are some other reasons that I’m headed towards…

However, before we move on to the next chapter in our little saga, it’s best to get really comfortable with the plots in Figure 7. I especially want you to get used to some specific things, in order of importance:

The frequency scale is circle – it’s not a straight line.

The scale starts on the right (at the 3 o’clock position) and goes counter-clockwise to the left (the 9 o’clock position).

The scale is linear, not logarithmic, like you’re used to seeing.

The maximum frequency is the Nyquist frequency, so it’s defined by the sampling rate.

Once the point on the circle goes beyond the Nyquist, we’ve started aliasing, and so we’ve entered a symmetrical world that mirrors the half below the Nyquist. (In other words, when we get a little farther, you’ll see that the top and the bottom of that circle are mirror images of each other – as I’ve already hinted in Figure 6 looking at the frequency range from 0 to 48 kHz.)

Most digital filters that are applied to audio signals use a “basic” building block called a “biquadratic filter” or “biquad” which consists of 2 feed-forward delays and 2 feed-back delays, each with its own output gain and a delay time of 1 sample. I’ve already talked a little about biquads in this posting, where I showed a couple of different ways to implement it. One of the standard ways is shown below in Figure 1.

The signal flow that I drew for Figure 1 is a little more modular than the way it’s normally shown, but that’s to keep things separate for the purposes of this discussion.

The two feed-forward delays add to the input signal (via gains b0, b1, and b2) and the result shows up at the red arrow. Remember from Part 1 that this portion of the biquad can only make a magnitude response that has (in an extreme case) infinitely deep, sharp dips, and smooth rounded peaks.

The signal from the red arrow onwards goes into the feed-back portion of the filter with two feed-back delays adding through gains -a1 and -a2. Again, remember from Part 1 that this portion of the biquad can make a magnitude response that has infinitely deep, sharp peaks, and smooth rounded dips.

Let’s say that we wanted to make a simple filter – let’s make it a low pass filter – using this biquad. How do we do it?

The simplest way is to cheat and go straight to the answer.

Cheating Option 1: You go to this page at www.earlevel.com and put in the parameters you’re interested in (Filter Type, sampling rate, Fc, Q, etc…) and copy-and-paste the resulting five gains (we’ll call them “coefficients” from now on).

Cheating Option 2: We search on the Interweb for the words “RBJ Audio Cookbook” and then spend some time copying, pasting, and porting the equations that Robert Bristow-Johnson bestowed upon us many years ago* into your processor. You then say “I want a low pass filter at 1000 Hz with a Q of 0.5, please” and the equations spit out the five coefficients that you seek.

However, if you cheat, you’ll never really get a grasp of how those coefficients work and what they’re really doing – and that’s where we’re headed in this little series of articles. So, you might decide to go through this series, and then cheat afterwards (that’s what I would recommend…)

Now, before you go any further, I’ll warn you – the whole purpose of this series is to give you an intuitive understanding. This means that there are things I’m going to (intentionally) skip over, merely mention in passing, or omit completely. So, if you already know what I’m talking about, there’s no point in reading what I’m writing – and there’s certainly no need to email me to remind me that I didn’t mention some aspect of this that you think is important, but I’ve decided is not. If you feel strongly about this, write your own blog.

In the previous posting, we left off with this drawing of a biquad filter:

This is not the normal way to draw the signal flow inside a biquad, since it has a little too much information. Normally you see something like this:

or this:

In the versions I show above, the feed-forward half of the biquad comes first, and its output feeds the start of the feedback portion. It is also possible to reverse these, putting the feedback portion first, like this:

In theory, these different implementations will all result in the same output if you match the gain values. However, in practice, they are not the same, and this difference is where we need to look for this part of the discussion on high res audio.

Let’s say I want to make a simple filter that reduces bass in a fairly narrow frequency band. I can use a biquad to do this. For example, if I want a peaking filter that reduces 20 Hz by 12 dB, with a Q of 1, then I get a magnitude response that looks like this:

If I wanted to build this filter using a biquad in a system with a sampling rate of 48 kHz, it would have the following gain coefficients:

We’ll also say that my biquad is implemented like the one shown in Figure 1, above… let’s take a look at that signal flow again:

I’ve highlighted a point inside the biquad using a red arrow. Let’s talk about the signal right there, in the middle of the processing…

In the last post, we talked about how, when the signal frequency is very low, a single sample delay has almost the same value at its output as its input, because the phase difference is so small for such a small time. So, let’s start with the (incorrect) assumption that, for those two feed-forward delays at the beginning, their outputs ARE equal to their inputs (because we’re starting with a low frequency). What happens when the input has a value of 1? Then the value at the red arrow is just the sum of the feed forward gains (because I multiplied each of them by 1 and added them together…)

In the case of the filter I described above, this value will be 0.000006836, which is a very small number. Also, if the value coming into the input of the biquad is less than 1, the value at the red arrow will be even smaller! This means that, if you come into the biquad with a low-frequency tone with a level of 0 dB FS, the level at that red arrow will be about -103 dB FS, which is very quiet. The feed-back portion of the biquad, after the red arrow, then has a lot of gain in it to bring the signal level back up towards 0 dB FS again.

So, the issue that we have here is that the FF (Feed Forward) portion of the biquad drops the level A LOT. And the FB portion increases the level A LOT, just to do something like a little 12 dB dip at 20 Hz.

The magnitude of the gains downwards and upwards in those two portions of the biquad are dependent on the parameters of the filter that we’re trying to make, however, we can generalise a little and say that:

the lower the frequency OR

the higher the Q,

then the bigger the gain down and up.

In other words, if you have a really low frequency dip, with a really high Q, then the level of the signal at that red arrow will be really low. REALLY low.

How low can you go?

How low is “REALLY low”? let’s see:

Take a look at Figure 7, which shows some values for one example filter (peaking, Gain = -12 dB, variable Q and Fc, and the test frequency = Fc). Notice that when the Fc is 10 kHz, even at earn Q=32, the signal level at the middle of the biquad is about -38 dB FS or so. However, when the Fc is 20 Hz, it’s -140 dB FS… This is very low.

Now let’s try again at a higher sampling rate: 192 kHz.

Notice that when we do exactly the same thing running at 192 kHz, the signal levels inside the biquad get much lower. Now for a 20 Hz signal and a Q of 32, the level is around -163 dB FS – a drop of more than 20 dB for 4x the sampling rate.

Why does this happen? It’s because the filter doesn’t “know” that the signal is at 20 Hz. It only knows the relationship between the frequency and the sampling rate. So, in its little world, 20 Hz doesn’t exist. In a system running at 48 kHz, what exists is 20 / 48000 = 0.0004167. This is called the “normalised frequency” where the sampling rate is 1, DC is 0, and everything else is in between. (Note that some textbooks and software say that Nyquist = 1 instead of the sampling rate – but you just need to know what the convention is for the thing you’re reading…) This means that if the sampling rate goes up to 192 kHz, then the normalised frequency for 20 Hz is 20 / 192000 = 0.0001042 (1/4 of the value because the sampling rate was multiplied by 4).

So what?

This is important. If you want to make a low-frequency, high-Q peaking filter in a digital system with a cut of 12 dB, you are forcing the signal to a very low level inside your filter, and then bringing it back up to a normal level again on the way out. If your processing is running with a limited resolution, (e.g. 16-bits, for example) then the signal level can approach or even go below the resolution of your system inside the biquad. This means that, when the signal’s level is raised again on the way out, it’s full of quantisation distortion, and you can’t get rid of it… This is bad.

There are different ways to solve this problem.

Increase the resolution of your processing internally. For example, even though your input and output might only be running at 16-bits or 24-bits, maybe you need more resolution inside to make the results of the math better – or at least below the limitations of the input and output.

Change the way the biquad is implemented. For example, if you use the implementation shown in Figure 4 (with the feedback before the feed-forward) instead of the one we used, then you don’t drop the signal level and raise it again, you do the opposite. This avoids your quantisation error problem. However, depending on the system, it might overload and clip the signal inside the biquad instead, so then you just end up with a different kind of distortion instead.

Reduce your sampling rate to make it closer to your filter’s frequency. The problem I showed above is that the centre frequency of the filter is too far away from the sampling rate. If the sampling rate were lower, then this automatically makes the filter’s centre frequency “higher” in a normalised frequency scale, thus reducing the problem.

Other, even more clever solutions that I won’t talk about because they’re not as simple.

This means (for example) that if you’re building a subwoofer with digital filtering, and you know for sure that NOTHING will come out of it above, say 1 kHz (just to pick a random number that’s far enough away from the typical 120 Hz that people normally use…) then it would be dumb to do the filtering at 192 kHz. It’s smarter to run its internal sampling rate at 2 kHz (because we only need to go up to 1 kHz; and we’re not considering anything other issues or artefacts in this posting.)

P.S.

For this discussion, I used the specific example of a peaking filter with a gain of -12 dB, and I was varying the Q and the Fc. I was also measuring the level of the signal using a sine wave with a frequency that was the same as Fc in each case. However, the general lesson here about low frequency and high-Q filtering holds for other filter types and implementations as well.

Today I was working on a little acoustics simulation patcher in Cycling 74’s Max, and part of the code required the use of a modulo function. No problem, right?

Problem. I originally wrote the code in Matlab, and I was porting it to Max; and the numbers just weren’t working properly. After getting rid of my own home-made bugs, it still wasn’t working…

Turns out that there seems to be a disagreement in the code community about how to do the modulo of a negative number.

The best indication of the problem I was facing is found on this page, where you can see that different languages come up with different answers for -13 mod 3 and 13 mod -3. The problem is that neither Max nor Matlab are in the list. So: here are the results of those two, to add to the list.

The results are:

Language

13 mod 3

-13 mod 3

13 mod -3

-13 mod -13

Matlab

1

2

-2

-1

Max

1

-1

1

-1

This means that Matlab behaves like Python, using the formula

mod(a, n) = a – n * floor(a / n)

whereas Max behaves like C and Java.

So, if you, like me, move back and forth between Matlab and Max, beware!

#1: You have to drive to a meet someone at a specific time. Let’s say that you only have to drive on one road to get there, and the speed limit is the same the whole way. You calculate the time it will take to get there on time, and you start driving – but there’s traffic. So, you wind up driving half the distance at half the speed, then the traffic disappears.

How fast do you have to go the rest of the way to arrive at the meeting on time?

#2: You’re driving on a two-lane highway where the speed limit is 70 km/h. You are driving 100 km/h, and you pull into the left lane to pass someone who is driving the speed limit. Everything about the car you’re passing is identical to yours – even the driver weighs the same as you do. At the instant that you are side-by side, a train appears across the road in front of you and stops. You both hit the brakes at exactly the same time to try and stop from hitting the train.

Luckily, the person in the other car stops just as his bumper touches the train, let’s say 1 mm before touching it… But, because you were driving faster, you cannot stop in time.

How fast are you going when you hit the train?

The answers

#1. Most people instinctively say “double the speed limit” to make up the lost time. However, this is not the right answer.

Let’s say that the meeting is 100 km away, and the speed limit is 100 km/h. Therefore, it should take you 1 hour to get to the meeting.

If you drive half the distance (50 km) at half the speed (50 km/h), then at the moment the traffic clears up, you should have been at your destination. So, you would have to drive infinity km/h to get there. However, since teleportation doesn’t exist yet, you might as well just call and tell them you’ll be late.

#2: This one is a little tougher, but it should be pretty intuitive for someone working in audio. A car’s brakes work by taking the energy in the car’s momentum, and converting that to heat in the brake discs. The key word there is energy.

So, the question is: if you consider the amount of energy removed from the car going 70 km/h, and take that out of the energy in the car going 100 km/h, how much energy is left?

The answer is 70 km/h. For someone in audio, this might look like a familiar answer, since 0.7 V has half of the power of 1.0 V (assuming identical loads). In the case of the cars, it’s because the amount of power (the amount of energy that’s transmitted over time – in this case, to heat the brakes) to bring the car from 70 km/h to 0 km/h is identical to the amount of power it takes to bring the same car from 100 km/h to 70 km/h. (An audio geek might joke that 70 km/h is 3 dB slower than 100 km/h.)

The conclusion

Slow down. You’re not going to make it to the meeting anyway, and driving a little bit faster means you’re going to hit the train much harder than you think.

These days, I’m spending a lot of time wrapping my head around the relationship between the frequency and the time responses of filters. In doing so, I’m digging into the concept of “Q”, of course. As a result, I’m reading my old books and some Internet sites, and I’m frequently presented with something like the following:

That, of course, is from the Wikipedia entry on “Q”.

However, in the Bell Telephone System Technical Publication – Monograph 2491, called “The Story of Q” by Estill I. Green ( published in the American Scientist, Vol 43, pp 584-594, in October 1955), it states:

“For a time, Johnson* designated the ratio of reactance to effective resistance of a coil by the symbol K. It was in 1920, while working the practical application of the wave filter which G. A. Campbell had invented some years before, that he for the first time employed the symbol Q for his parameter. His reason for choosing Q was quite simple. He says that it did not stand for ‘quality factor’ or anything else, but since the other letters of the alphabet had already been pre-empted for other purposes, Q was all he had left.”

So, if we’re going to be pedantic (which I love to be) there are two errors on that Wikipedia page. Firstly, Q does not stand for Quality. Secondly, it’s not the “Q factor”, it’s just the “Q”.

As an aside, that monograph is not only informative, it’s fun to read (depending, of course, on your definition of “fun”). For example, near the end of the paper, Green applies Q to rotating bodies (which is not a surprise, since an audio-wave oscillation is just a rotation represented in two dimensions). In that section, he points out that the rotation of the earth is slowing down due, in part, to tidal friction. Consequently, the length of a day is increasing at a rate of 0.00164 second per century, which would make the Q of the rotation of the earth equal to about 10,000,000,000,000 (10^13).

* K.S. Johnson worked in the Western Electric Company’s Engineering Department, which became Bell Telephone Laboratories in 1925.