Scrabble on Mobile

I’ve been playing Words with Friends with family, both near and far, for the past year or so. It’s similar to Scrabble but the scoring and rules are different enough I wanted to try good old fashioned Scrabble. I discovered that Hasbro and Electronic Arts collaborated to create a mobile version of Scrabble where the gameplay is similar to Words with Friends.

What’s Wrong with WWF?

The Words with Friends rules favor frequent players, which can be unfair. For example, it has short 5-move games you can play with its AI, and doing this earns you credits you can use to buy swaps and other advantages when playing other people. Also, WWF is generally easier than Scrabble encouraging crazy big plays. For example, every time you prepare a move it shows a bargraph showing how good that move is compared to the best available, so you know whether there’s a bigger scoring move, how much bigger it is, and whether it’s worth taking more time before submitting your play. Finally, the Scrabble app uses the official built-in Scrabble dictionary, where WWF has its own dictionary that is frustratingly inconsistent.

Scrabble is available on iOS and Android and it has the same rules & scoring as the good old board game you remember. And the familiar consistent dictionary. Once you get it set up and you log in, it works quite well. But getting there is much more difficult than it needs to be.

Where Are My Friends?

Installing is easy enough. But once installed, if you want to play with your friends, you all need to create accounts. Scrabble offers Facebook, but since I don’t have a FB account, I used the alternative option to create an Electronic Arts (EA) account.

I did this for both Michelle and myself. Then in the app I clicked “New Game”, then “Play with Friends”. The list of friends was empty, as expected since this was my first time. I tapped the “Find player” searchbox to enter her username but the phone’s keyboard didn’t appear, so I couldn’t enter anything. This was confusing: what is the point of a “Find player” search box, if it doesn’t let you type in anything to search for? I tried this on Michelle’s phone too, same behavior.

Then I googled the problem. Apparently, lots of people encounter this problem. With further reading and experimenting, here is the workaround that I cobbled together:

Key Facts

  • There is a site called Origin, owned by Electronic Arts.
  • When you create an EA account, it is also an Origin account.
  • In Scrabble, you can only play with people you have befriended on Origin.
  • Origin is both a web site, and a fat client application on Windows and Mac.
  • Finding and befriending other players can only be done in the application, not on the web site.

Workaround Steps

  • Point your browser at EA create a user account.
  • Point your browser at Origin and download the fat client (Windows or Mac).
    • Since I run Linux, I used my Windows 10 VM running on VirtualBox.
  • Install the client app, run it, and log in as your EA user.
  • In the app, use the “find friends” feature to find your friend(s).
    • You need to know their username or email.
  • For each one, click on them and send a friend request.
  • Your friends must follow the above steps, then accept your friend request.

After the above is complete, run Scrabble on your device. Pick “Create New Game”, then “Play With Friends”. Your friends should now appear in the list. Pick one and play!

Why Can’t I Submit my Move?

I’m in a game with Michelle and I submit a move. The submit button transforms to say “waiting”… and the app just hangs. A few minutes goes by, nothing. I close the app, open it again, and my move is gone as if I had never made it. I make the same move again, and submit it. Same behavior.

Long story short, the app had logged me out. But it didn’t tell me. And it just stopped working without any error message or indication why. Poking around, I went to settings and happened to see that I wasn’t logged in anymore. I logged in again. Then the app started working.

So, the app occasionally logs you out without telling you, and misbehaves for no apparent reason. Keep that in mind. If the app every acts strangely, go to settings and double-check your login status.

Conclusion

What’s really frustrating about this process is how obscure it is. The average person:

  • Won’t know why the app “Find player” search box doesn’t work.
  • Won’t know that EA is associated with Origin.
  • Won’t know that they can only find friends on Origin.
  • Won’t know that this feature only works in the Origin app, not the web site.
  • Won’t know that the app occasionally logs them out.

None of this is explained in the app or in help that I could find online. The first part is a one-time setup thing so that’s no a problem if you know to do it. The second part you just need to know about. But once you get past these, the app is pretty good. It’s fun to play old-fashioned Scrabble without any player aids which is more balanced and fair.

Loudness Wars and Classical Music

Note: it turns out that my PC had a background app that was boosting the level by +10 dB. This didn’t show up in the audio panel, which had everything set to flat / zero. There was nothing wrong with this recording. However, I’ll leave this here since it talks about how to identify overly hot recordings and fix them as much as possible.

Until recently, classical music has been free of loudness wars nonsense. Most classical music recordings are made with maximum transparency, with little or dynamic range compression, equalization, or other processing. Classical music recordings still sound quite different, but the differences are due to the room, how it’s miced, types of mics, etc. Post-processing is kept to a minimum compared to other genres.

However, as an Idagio subscriber I’ve been listening to a wide variety of different music and recordings and recently found some that make me worry about this. Here is one example, and a few steps I took to “correct” it in Audacity. I use that word loosely because clipping loses information and any restoration is at best mathematically educated guesswork.

The recording is the Brahms Piano Trios played by Ax, Ma and Kavakos recorded on Sony in 2017. You can find it Idagio, Amazon and other places. When I first started listening to it I thought it was a great performance but it seemed a bit loud; I had to turn down the volume to a lower position than I normally use. Then, when the first crescendo came it sounded just a bit harsh and distorted. Not obvious, but just a bit “strained” sounding.

Out of curiosity I loaded the track into Audacity and this is what I saw:

Oops, that doesn’t look good. Let’s turn on “view clipping”:

Yowza! Those engineers really blasted this recording. Let’s zoom in on one of those clipped parts:

Yep, that is some serious clipping. This is not just intersample overs, it is actual honest-to-goodness clipping. They definitely over-baked this recording. Let’s shift the level down by 6 dB, then apply the “Clip Fix” tool with a threshold of 99%.

Holy smokes Batman! Even after a 6 dB reduction, restoring the peaks still clipped! Those engineers really blasted this recording. Let’s undo the clip fix, undo the 6 dB reduction, then reduce it by 9 dB and do another clip fix:

OK, that’s looking better. Now let’s look at the entire track, with view clipping enabled:

Good. After applying -9 dB and clip fix to every track, the new peak level was near -1 dB. So all was good. On listening, that harsh strained sound in the crescendos is gone. But of course, this doesn’t actually fix the problem. When the music is clipped, information is forever lost. We don’t know the shape of the waveform when it exceeded 0 dB. All clip fix does is restore a smooth curve which avoids the harsh sound of the sharp edge transitions of clipping.

Passive Attenuators

Introduction

This is about passive attenuators. Sometimes called “passive preamps”, they are switchboxes with volume controls that typically have 24 to 48 discrete positions. Back in ’00 I designed and built one, and used it daily for over 10 years.

Passive attenuators get a mixed reaction from audiophiles. Some say they are the most transparent way to listen to music, better than any active preamp at any price. Others say they sound un-dynamic and flat. Audiophiles with EE backgrounds also have a mixed reaction to them. Some say they are transparent, others say they have high noise and non-flat frequency response.

In this article I’ll describe

  • System requirements for a passive to work well
  • How a passive actually works
  • Measurements of noise and frequency response comparing their performance to the best active preamps
  • Comparison to active preamps

1. System Requirements

It turns out all the above views have some thread of truth. How well a passive works depends on the system in which it is used. Here are the requirements:

  • Upstream devices (sources) have low output impedances
  • Downstream devices (destinations) have high input impedances
  • Short cables having low capacitance
  • Sources are “loud” with enough gain to drive destinations to full power
    • Put differently
      • You don’t need gain, you only need attenuation.
      • If you plug your sources directly into your power amp, it will drive it to extra loud levels you will never actually use.

Most solid state components and well engineered cables meet these requirements. A system that doesn’t meet these requirements is the exception, not the norm.

2. How a Passive Attenuator Works

A passive attenuator is a simple voltage divider. The source device signal is a voltage swinging from + to -. Send this voltage through 2 resistors in series, R1 and R2. The downstream device receiving the signal is in parallel with R2.

The voltage will have some drop across R1, and some drop across R2. How much it drops across each resistor depends on their impedance ratios. This determines the volume setting: how much it attenuates the signal.

The passive attenuator’s volume knob usually has 24 switches about 2 dB apart, or 48 switches with smaller steps. Each position puts 2 different resistors in the signal path.

Before going further, let’s mention 2 simplifying assumptions:

  • The source device output impedance is zero
  • The destination device input impedance is infinite

These are not actually correct, but they are close enough. Most solid state sources have output impedances around 10 to 100 ohms. Most solid state amps have input impedances around 10,000 to 50,000 ohms.

2a. Source Load

The passive attenuator shows the same load (impedance) to the source device at every volume position. So the source doesn’t “care” what volume position you are using. Make this load high enough that it is easy for the source to drive it, but no higher. The source has to swing a voltage back and forth, and the higher the load impedance, the less current it draws. So higher impedance is an easier load. But too high an impedance creates higher noise (more on that later).

A 10k attenuator means R1 + R2 = 10,000 ohms at every volume position. A 5k attenuator mean they sum to 5,000 ohms. The most popular attenuator is 10k, though 5k and 20k are also used. From here on we’ll talk about 10k, but the reasoning can be applied to any value.

As a general rule, you want at least a 1:10 ratio from the source to the load. If the source has a 100 ohm output impedance, it wants to drive a load of at least 1,000 ohms. Typical solid state sources are less than this, so a 10k attenuator gives more than 1:100 ratio which is more than sufficient. If all your sources are under 500 ohms output impedance, then you should use a 5k attenuator.

Since R1 and R2 are in series, the total load the source sees is R1 + R2. Of course it’s a little less than this since the destination device is in parallel with R2 which lowers the resistance across R2. But its input impedance is so high it doesn’t materially affect it.

So now we have the first rule of a passive attenuator: each pair of resistors R1, R2, sum to 10,000 (or 5k, or 20k).

2b. Attenuation

We mentioned earlier that the ratio of R1 to R2 determines the attenuation. Here I’ll explain exactly what that means.

At every volume position, the total load is 10,000 ohms. If R1 makes up half of that, then half the voltage drops over R1 and the other half drops over R2. In this case, if the source signal is 2 V, then 1 V drops over R1 and 1 V drops over R2. If R1 makes up 75% of that, then 75% of the voltage drops over R1 and 25% drops over R2. In this case if the source signal is 2 V, then 1.5 V drops over R1 and 0.5 V drops over R2.

We convert these ratios into dB with the standard formula

20 * log(ratio) = dB

More on that here.

It just so happens that the first example above is -6 dB of attenuation, and the second is -12 dB. That is:

20 * log(0.5) = -6
20 * log(0.25) = -12

Converting this intuition into math, this leads to the formula:

Attenuation Ratio = R2 / (R1 + R2)

Since R1 + R2 is always 10,000 this gets even simpler. If you want to attenuate the signal to, say, 17% of its original value, use a 1700 ohm resistor for R2, then R1 will be the difference between that and 10,000.

This is all there is to designing a passive attenuator — at least, to selecting the resistors for each volume position. Their ratio determines the attenuation, and their sum is always 10,000.

2c. Wrap Up

What input voltage does the downstream device see? It’s the output voltage of the attenuator. The circuit diagram makes it obvious:

The downstream device is in parallel with R2, so it sees the same voltage. The voltage drop across R2 is the output voltage, which will always be equal or less than the source voltage (since some of the voltage will drop over R1).

The diagram shows resistors for -32 dB of attenuation, or the output being 2.5% of the input.

Example: let’s compute the first few highest volume settings for a passive attenuator having 24 positions each 2 dB apart.

Position 1: full volume. Here, R1 is zero – just a straight wire and R2 is 10,000 ohms. The entire signal (2 V or whatever) drops across R2.

Position 2: -2 dB. First, compute the ratio for -2 dB. Reversing the above formula we get:

10^(-2/20) = 0.7943

This means R2 is 7,943 and R1 must be 2,057.

Position 3: -4 dB. Our ratio is 0.631, so R2 is 6,310 and R1 is 3,960.

Now resistors aren’t available in arbitrary values. You would look at the parts list and find resistors that come closest to the values you want. In practice, when designing an attenuator you can usually get the steps within 0.1 dB and keep the total resistance within 100 ohms (or 1% of your target value).

Congratulations – you can now design a passive attenuator!

The next question is: why would you use one? One part of that answer is low noise at low volume settings.

3.1 Noise

Resistors add noise to the signal. How much noise depends on the type of resistor; some are noisier than others. There is a theoretical minimum amount of noise that any resistor can have; all resistors have at least this much, in fact more. This noise has 3 common names: thermal, Johnson, and Nyquist. But whatever you call it, it is the same thing: the heat energy from the resistor’s temperature, randomly exciting electrons that appear as tiny voltages. We’re talking super tiny here. For our application, it is in micro-Volts (millionths of volts). This noise spans all frequencies, so the amount of noise that is relevant to our application depends on the bandwidth. In audio, let’s assume bandwidth is 20,000 Hz.

A passive attenuator introduces other kinds of noise too. Resistor composition noise, junction/contact noise, etc. To minimize these noises, use high quality contacts and “clean” resistors. The cleanest resistors are wire wound and metal film. These resistors have actual real-world noise so close to the theoretical minimums, we can use those minimums in our noise computations.

For example, thermal noise of a 10,000 ohm resistor at room temperature in audio bandwidth is about 1.8 uV, or 1.8e-6 volts. A 100 ohm resistor is 0.18 uV, or 1.8e-7 volts. Dropping the resistance by a factor of 100 drops the noise by a factor of 10. If the signal (voltage drop) over the resistor is 1 V, this is -115 and -135 dB SNR respectively. The first is comparable to the noise in the very best active preamps, the second is better than any active preamp. However, if we reach a quiet part of the music and the signal drops 30 dB quieter, the noise level remains constant so the SNR drops by 30 dB and it’s 85 dB and 105 dB respectively.

3.1.1 Noise: Absolute or Relative

When you use a thermal noise calculator you’ll find that resistor noise is measured in 2 ways: as a voltage, and as a voltage ratio. The astute reader will wonder: It can’t be both, so which is it? In other words: Is resistor noise inherently a ratio, so if you apply a smaller voltage across the resistor you get less noise, and the SNR remains constant? Or is resistor noise inherently a constant, so if you apply a smaller voltage across the resistor, the signal is smaller relative to the noise and the SNR drops?

Sadly, for our purposes building passive attenuators, resistor noise is inherently a constant. It is the same regardless of the voltage across or current through the resistor. This suggests that noise is unlikely to be an issue at max volume, but it may become an issue as we turn down the volume.

3.1.2: Noise From What Resistor?

OK so we can compute noise but we’re still not out of the woods. When computing the noise added by a passive attenuator, it’s not obvious which resistor, or more generally what impedance, to use!

For example consider the above circuit diagram. The signal passes through both R1 and R2, so intuition says each one adds noise. The total noise should be the sum of the noise from each. But that sum is always 10,000 ohms, so the noise would always be 1.8e-6 volts. But this simple intuitive approach is incorrect.

3.1.3: Output Impedance

The solution is to view this from the perspective of the destination device. Just like the output voltage that matters is the voltage across the destination device’s terminals, the impedance that matters for noise computation is the impedance that the destination device sees. This is called the output impedance of the passive attenuator. Imagine you are at the input terminals of the destination device looking upstream toward the source. What impedance do you see?

Going from + to – upstream, you see R2 in parallel with (R1 and source output impedance in series) . In other worse, the passive attenuator’s output impedance is:

1 / ((1 / R2) + ((1 / R1 + SourceOutput)))

Since output impedance is typically very small, this is close to R2 and R1 in parallel, which is:

1 / ((1 / R1) + (1 / R2))

When R2 and R1 are very different, this is roughly equal to the smaller of them. When R1 and R2 are nearly equal, this is roughly equal to half of either of them.

This is the impedance that we use to compute the noise added by the passive attenuator.

Important note: remember the requirement that the destination device have a high input impedance? You want another 1:10 ratio here. That is, the input impedance of the amp (or your downstream destination device) should be at least 10 times higher than the output impedance of the passive attenuator. The worst-case highest output impedance is when R1 and R2 are equal, 5,000 ohms each at -6 dB. Here the output impedance is 2,500 ohms. So the amp should have an input impedance of at least 25 kOhm.

If it doesn’t, then use a 5k attenuator. But the lower impedance makes it harder to keep the 1:10 ratio on the input side. However, it’s still pretty generous since most solid state sources have output impedances well under 500 ohms.

3.1.4 Computing Noise

Let’s compute the passive attenuator noise from our example above at 0 dB, -2 dB and -4 dB.

At 0 dB, the 2 output impedance legs are 10,000 ohms, and zero. Well not quite zero, but the output impedance of the source device. Let’s suppose that’s 100 ohms. The output impedance will be close to 100 ohms. But more precisely:

1 / ((1 / 10000) + (1 / (0 + 100))) = 99 ohms

Thermal noise of 99 ohms (at room temp and audio bandwidth) we’ve already computed above at 1.8e-7 volts. Also at 0 dB we have the full scale signal from the source, which is 2 V at its loudest which gives us a SNR of:

20 * log(1.8e-7 / 2.0) = -141 dB

Wow! No active preamp achieves that! And it’s probably even better because the output impedance of solid state sources is usually closer to 1 ohm than 100 ohms. Let’s check the SNR when the music (source voltage level) reaches a quiet part, say 30 dB lower, which is 31.6 mV.

20 * log(1.8e-7 / 0.0316) = -105 dB

Well, we really didn’t have to do the math there. Thermal noise is constant and the signal dropped by 30 dB, so the SNR drops by 30 dB. That’s a big drop, but it’s still very good. Again, it’s probably better in the real world because it depends on the the source output impedance will will probably be closer to 1 ohm than 100.

At -2 dB the R1 & R2 resistors are 2,057 and 7,943 ohms. The output impedance will be:

1 / ((1 / 7,943) + (1 / (2,057 + 100))) = 1,696 ohms

Thermal noise of 1,696 ohms is 7.41e-7 V. Per the above, at -2 dB the output is 79.43% of the input. So voltage across R2 (the output voltage) for a 2 V source signal is 1.5886 V. Thus the SNR is:

20 * log(7.41e-7 / 1.5886) = -127 dB

If the music reaches a -30 quiet part, it’s 30 dB worse which is -97 dB.

Now let’s skip -4 dB and use a more realistic listening level. Nobody listens that loud. Typical attenuation for actual listening with a power amp or headphones is around -30 dB. Of course this is a very rough figure depending on amp gain, speaker efficiency, room size and listener preferences. But it’s in the ballpark.

At -30 dB the attenuation is:

10 ^ (-30/20) = 0.03162

So the R2 resistor must be 3.162% of 10,000 which is 316 ohms. That means R1 must be 9,684 ohms. This means the output impedance is:

1 / ((1 / 316) + (1 / (9,684 + 100))) = 306 ohms

Thermal noise at 306 ohms is 3.15e-7 V. At -30 dB the output is 3.162% of the input. So voltage across R2 for a 2 V source is 0.06324 V. Thus the SNR is:

20 * log(3.15e-7 / 0.06324) = -106 dB

And if the music reaches a part 30 dB quieter, that’s -106 – 30 = -76 dB.

4. Comparison to Active Preamps

Most active preamps have a fixed gain stage with attenuation. Usually the attenuation is upstream from the gain, because that helps prevent input voltage clipping. But it has the drawback that any noise added by the attenuation potentiometer is amplified by the gain ratio. Furthermore, the amount of noise, which depends largely on the gain ratio, is constant regardless of the signal level. This means as you turn down the volume, the SNR drops with it.

The SNR of amps and preamps is measured at full output. But this is misleading, since nobody actually listens at full output. With typical listening levels 20 dB below full output (for speakers) or 30 dB below full output (for headphones), the SNR you actually hear when listening is 20 or 30 dB less than advertised.

You can see this in practice on many of the reviews at Audio Science Review. The SNR at 50 mV output is typically 30-40 dB lower than the SNR at full volume.

Consider an ultra-high quality active preamp having an SNR of 120 dB at full scale 2.0 V output. When you turn it down to a typical listening level, say -30 dB, the SNR drops to the high 80s. If you took the full scale output of that preamp and sent it to a passive attenuator having the same 30 dB of attenuation, the SNR would be 106 dB. The passive attenuator is 20 dB quieter than the active preamp.

In summary, at full volume a passive attenuator has no advantage. But at the lower levels that we actually listen, they have:

  • Lower noise.
  • No added distortion.
  • Perfectly flat frequency response at all audio frequencies.

4.1 One Exception

Active preamps can be designed to have better performance (lower noise) at low volume settings. One way is to put the volume potentiometer downstream from the gain stage. This has 2 advantages: first, pot noise is not amplified by the gain ratio. Second, it attenuates the signal after the gain noise has been added, so it attenuates both the signal and the noise. The drawback is that this exposes the gain stage directly to the source voltages, so it will clip if those voltages are too high.

The JDS Atom is an example of this design and you can see how it improves its low volume performance. At 2 V its SNR is 120 dB, and at 50 mV it is 92 dB. As you turn the volume down by -32 dB, the SNR drops by 28 dB. This is less than 1:1, where most preamps are more than 1:1. A passive attenuator would still be a bit cleaner, but the Atom outperforms most other preamps at low volume settings, and the improvement that a passive attenuator gives may be immaterial.

Harmonic Content, Bass and Energy

Background

Most of the sounds we hear are made up of many different frequencies all vibrating together at the same time. The energy in a wave depends on its amplitude and frequency. The higher the amplitude, the more energy. With sound, amplitude is related to loudness. Also the higher the frequency, the more energy. The amplitude part of this makes intuitive sense. The frequency part does too, but it is less obvious.

Consider a musical instrument playing a sound. Since energy depends on amplitude and frequency, if it puts equal energy into all the frequencies it emits, then the higher frequencies must have a smaller amplitude. Musical instruments don’t actually put equal energy at all the frequencies they emit, but this does hold true roughly or approximately. If you do a spectrum analysis, they are loudest at or near the fundamental (lowest) frequency and their amplitude drops with frequency. Typically, roughly around 6 dB per octave. That is, every doubling of the frequency roughly halves the amplitude.

For example, here is amplitude vs. frequency for a high quality orchestral recording:

This graph shows amplitude dropping as frequency increases. Since energy is based on amplitude and frequency, this means roughly constant energy across the spectrum (all frequencies).

This implies that low frequencies are responsible for most of the amplitude in a musical waveform. So, if you look at a typical musical waveform, it looks like a big slow bass wave with ripples on it. Those ripples are the higher frequencies which have lower amplitudes. Further below I have an example picture.

Audio Linearity

Audio devices are not perfectly linear. They are usually designed to have the best linearity for low level signals, and as the signal amplitude approaches the maximum extremes they can become less linear. This is generally true with analog devices like speakers and amplifiers, and to a lesser extent with digital devices like DACs.

For example, consider a test signal like 19 and 20 kHz played simultaneously. If you encode this signal at a high level just below clipping, it’s not uncommon for DACs to produce more distortion than they do for the same signal encoded at a lower level like -12 dB. I’ve seen much smaller level changes, like a 1 dB reduction in level giving a 30 dB reduction in distortion! The same can be true for amplifiers.

Furthermore, the lower the level of a sound, the fewer bits remain to encode it. 16-bit audio refers to a full scale signal. But a signal at -36 dB has only 10 bits to encode it because the 6 most significant bits are all zero. Because the high frequencies are usually at lower levels, they are encoded with fewer bits, which is lower resolution. The Redbook CD standard had a solution to this called pre-emphasis: boost the high frequencies before digital encoding, then cut them after decoding. This is no longer used because it reduces high frequency headroom and most recordings are made in 24 bit and are dithered when converted to 16-bit.

The Importance of Bass Response

One insight from the above facts is that bass response is more important than we might realize. At low frequencies (say 40 Hz), the lowest level of distortion that trained listeners can detect is around 5%. But at high frequencies (say, 2 kHz), that threshold can be as low as 0.5%.

So one could say who cares if an audio device isn’t perfectly linear? Because of the energy spectrum of music, the highest amplitudes that approach non-linearity are usually in the bass, and we’re 10 times less sensitive to distortion in the bass, so we won’t hear it.

But this view is incorrect. It is based on a faulty intuition. The musical signal is a not a bunch of frequencies propagating independently. It is a single wave with all those frequencies superimposed together. Thus, the high frequencies are riding as a ripple on the bass wave. If the bass wave has high amplitude approaching the non-linear regions of a device, it is carrying the lower amplitude along with it, forcing even those low amplitude signals into the non-linear region.

A picture’s worth 1,000 words so here’s what I’m talking about, a snippet from a musical waveform. The areas marked in red are the midrange & treble which is lower amplitude and normally would be centered around zero, but riding on top of the bass wave has forced them toward the extreme positive and negative ranges:

Speaker Example

This reminds me of a practical example. Decades ago, I owned a pair of Polk Audio 10B speakers. They had two 6.5″ midrange drivers, a 1″ dome tweeter, and a 10″ tuned passive radiator. The midrange drivers produced the bass and midrange. As you turned up the volume playing music having significant bass, at some point you started hearing distortion in the midrange. This is the point where the bass energy is driving the 6.5″ driver excursion near its limits where its response goes non-linear. All the frequencies it produces are more or less equally affected by this distortion, but our hearing is more sensitive in the higher frequencies so that’s where we hear it first.

Obviously, if you turn down the volume, the distortion goes away. However, if you use EQ or a tone control to turn down the bass, the same thing happens – the distortion goes away. Here the midrange frequencies are just as loud as before, but they’re perfectly clear because the distortion was caused by the larger amplitude bass wave forcing the driver to non-linear excursion.

Other Applications: Headphones

The best quality dynamic headphones have < 1 % distortion through the midrange and treble, but distortion increases at low frequencies, typically reaching 5% or more by the time it reaches down to 20 Hz. The best planar magnetic headphones have < 1% distortion through the entire audible range, even down to 20 Hz and lower.

Most people think it doesn’t matter that dynamic headphones have higher bass distortion, because we can’t easily hear distortion in the bass. But remember that the mids and treble are just a ripple riding on the bass wave, and most headphones have a single full-range driver. If you listen at low levels, it doesn’t matter. But as you turn up the volume, their bass distortion will leak into the mids and treble and become audible.

Thus, low bass distortion is more important in a speaker or headphone, than it might at first seem.

Other Applications: amplifiers and DACs

Amplifiers and DACs have a similar issue, though to a lesser extent. This concept could apply here as well – especially when considering the dynamic range compression that is so often applied to music these days.

Consider a digital recording that is made with dynamic range compression and leveled too hot, so it has inter-sample overs or clipping. Sadly, this describes most modern music rock/pop recordings, though it’s less common in jazz and classical.

Most of the energy in the musical waveform is in the bass, so if you attenuate the bass you reduce the overall levels by almost the same amount. This will entirely fix inter-sample overs, though it can’t fix clipping. Remember the 19+20 kHz example above? With most music, attenuating the bass will fix that too, since the higher frequencies are usually riding on that bass wave.

In an amp, the idea would be to attenuate the bass before the gain-feedback loop, then boost it back to normal afterward. In a DAC, the idea would be to attenuate the bass digitally before converting to analog, then boosting the bass back to normal in the analog domain.

Doing this essentially makes a tradeoff: lower distortion in the midrange and treble, higher distortion in the bass. Psychoacoustically, this tradeoff should be a net win because it prevents big low frequencies from leaking distortion into the midrange and treble.

VueScan Multi-Crop – How To

Continued from a few years ago … VueScan is a great scanning app but it has a UI that only an engineer could love. Multi-Crop is a feature where you can scan several things at once on the scanner deck and have them saved as separate files. I use this to scan 35mm film, since my scanner can load 12 frames at a time. While this feature is very useful, it’s hard to figure out how it works. Here, I describe how I use this feature to scan 35mm film with my Epson V600 which has a tray that loads 12 at a time.

First, load your media in the scanner. For this, I use the 35mm film negative tray and load up 2 parallel strips each having 6 photos.

Next, turn on the scanner, start VueScan, and make the right settings:

  • input mode: Transparency
  • input media: Color Negative
  • input bits per pixel: 24 bit RGB
  • input batch scan: List
    • This will make it scan each cropped sub-image and save as a separate file
  • input scan resolution: 3200 dpi (anything higher is overkill for most film negatives)
  • JPEG, quality 95 (anything higher is overkill for most film negatives)
  • output default folder: make sure it exists
    • else VueScan won’t save the pictures and it won’t give you any error message

Now hit the Preview button. It will scan then show the slides all together. With this displayed you can set up the Color tab. Read the fine print along the edge of the film to get the vendor, brand and type.

  • negative vendor: Kodak (or whatever)
  • negative brand: GOLD (or whatever)
  • negative type: 400 Gen 5 (or whatever)

You will set the rest of the image settings later, for each individual picture. Now go to the Crop tab. For 35mm film in my scanner’s tray, VueScan’s 35mm film setting works and is simpler.

  • crop size: 35mm Film
  • multi crop: 35mm Film

A grid will appear over the scan preview. It shows a dotted line rectangle over each of the slides. It won’t be perfectly lined up, but as long as it’s reasonably close it’s OK because you’ll fix that next.

Note: if it’s not even close, or if you haven’t filled the entire tray and you don’t want to waste time scanning blanks, you can select CUSTOM and do your own layout (rows, columns, sizes).

  • Note the blue <- and -> arrows at the bottom right of the screen; right of the image rotate buttons.
    • These move the focus forward and back across the sub-images of the multi-crop.
  • Click the left <- button until it disappears (so you can only shift right -> not left), to shift focus to the 1st image.
  • When focused on each image, VueScan remembers the settings you make for that image.
  • Click the + button (lower right of UI) to zoom so this single image fills the screen.
  • In the image preview, click inside the image near a corner, then drag a rectangle to mark the crop area to contain the image.
  • If needed, click the rotate buttons (lower right of UI) to ensure the image is right-side-up.
  • Go to the Color tab and ensure the exposure is right. The key controls for this are:
    • Black point: what % of the pixels are mapped to black (lowest intensity).
      • I find that 0.1% usually works well.
    • White point: what % of the pixels are mapped to white (highest intensity).
      • I find that 0.5% usually works well.
    • Curve low & high: set the shape of the contrast curve
      • Low and high are the 25th and 75th percentiles
      • If you set them to .25 and .75, you get a 1:1 linear mapping
      • To increase mid-intensity contrast, at the expense of losing detail in the darkest and lightest parts of the image: increase the low, decrease the high
  • Images on old film often look washed out. To fix this, go to the Filter tab and check one of these options:
    • Restore colors
    • Restore fading
  • After you’re done with image, click the blue next -> arrow to focus on the next image.
  • Repeat the above steps to set the crop box for each image.
  • When you’re done, click the previous <- arrow to review each of the images, ensuring your settings are correct.

Now click the “Scan” button at the bottom left of the screen. VueScan will scan each image, which can take about 3 minutes per image (over 30 minutes for a deck of 12). This is fully automatic so you can walk away and come back later to check the results.

Corda Soul & WM8741 DAC Filters

The Corda Soul uses the WM8741 DAC chip. Actually, it uses 2 of them, each in mono mode which gives slightly better performance. This chip has 5 different anti-aliasing reconstruction filters. The Corda Soul has a switch to select either of 2 different filters. Here I describe these filters, show some measurements I made, and from this make an educated guess which 2 of these filters the Corda Soul uses, at various sampling rates. At higher sampling frequencies the digital filter should make less difference; more on that here. My measurements and observations below are consistent with that.

Note: this DAC chip has a mode called OSR for oversampling. The Soul uses this chip in OSR high, which means it always oversamples the digital signal at the highest rate possible, to 192 or 176.4 kHz, whichever is an integer multiple of the source. For example, 44.1k is oversampled 4x to 176.4k and 96k is oversampled 2x to 192k. The function of the digital filters depends on this OSR mode.

Summary: the filters have 3 key attributes:

  • Frequency Response: how fast (sharp) or slow they attenuate high frequencies.
  • Frequency Response: the filter stop-band – is it above, at, or below Nyquist.
  • Phase: whether the filter is linear (constant group delay, FIR) or minimum phase (variable group delay, IIR).

This table summarizes key filter attributes – taken from the WM8741 data sheet linked above, for 44.1k / 48k sampling in OSR high mode.

NameRatePhasePassbandStopbandNyquistGroup Delay
1sharplin [min?]20,021 / 21,79224,079 / 26,208-6.0243
2slowmin [lin?]17,993 / 19,58423,020 / 25,056-28.078
3sharplin20,021 / 21,79224,079 / 26,208-6.437
4slowmin18,390 / 20,01622,050 / 24,000-116.1947
5slowlin18,390 / 20,01622,050 / 24,000-122.68

Note: at 44.1 kHz sampling, filters 1 and 3 are almost identical. The first is called “soft knee” while the third is called “brickwall”. Yet strangely, their frequency response is the same (despite their names which suggest otherwise) and the only difference is that 1 has more group delay. This suggests that the labels for filters 1 and 2 might have been mistakenly reversed in the WM8741 data sheet. Brickwall is usually the standard sharp filter closest to the ideal mathematical response. But not here, because being only -6 dB at Nyquist, it can allow ultrasonic noise to leak into the passband.

Filters 4 and 5 are labeled as apodizing. From what I read, this means their stop-band is a little below Nyquist. Why set the stop-band below Nyquist? Theoretically this is unnecessary. The reason given is that rejecting the upper band just below Nyquist is supposed to be an extra-safe way of avoiding any distortion introduced by the AD conversion during recording. Here, the stop-band of the apodizing filters is at Nyquist, but that’s still a bit lower than the others which are above Nyquist (which is an improper implementation).

Based on the above chart, filter 5 is the most correct implementation because it is the only filter that is fully attenuated by Nyquist, with flat phase response (minimal group delay). However, filter 5 rolls off a little early to achieve this. If you want flat response to 20 kHz, filter 3 is the best choice, though it does so at the price of allowing some noise above Nyquist. If one wanted a minimum phase alternative, the best choice would be filter 4. Both 1 and 4 are minimum phase, but 1 is not fully attenuated at Nyquist. Filter 4 is. However, to achieve this, filter 4 sacrifices FR with an earlier roll off.

For comparison, here’s how these filters behave at 96k / 88.2 k sampling (also in OSR high mode).

NameRatePhasePassbandStopbandNyquistGroup Delay
1sharplin [min]19,968/18,34648,000/44,100-120.4117
2slowmin [lin]19,968/18,34648,000/44,100–120.89
3sharplin40,032/36,77948,000/44,100-116.8948
4slowmin19,968/18,34643,968/40,396-126.829
5slowlin19,968/18,34643,968/40,396-130.528

At these higher sampling rates, all the filters are fully attenuated by Nyquist (or lower). That’s a good thing and Wolfson should have done this at the lower rates too. Also, filters 1, 2, 4 and 5 (all but 3) take advantage of the higher sampling frequency to have a wide transition band with gentler slope. This sacrifices response above 20k (which we don’t need) to minimize passband distortion, particularly phase shift. The numbers reflect this, as they all have flatter (better) phase response than filter 3.

As with the first table, filters 1 and 2 look like a mis-print; both have the same transition and stop bands. But all else equal, linear phase should have less phase shift, not more. This is probably a typo, because as you’ll see below, the impulse response for filter 1 is asymmetric, and for filter 2 is symmetric, and symmetric impulse response usually implies linear phase.

Based on this data, filters 2, 3 or 5 are the most correct implementations. Filter 3 has flat FR up to 40 kHz, but this extra octave comes at the price of a narrower transition band having more phase shift and group delay. Filters 2 and 5 have flatter phase response but start rolling off around 20 kHz to get a wider transition band. If one wanted a minimum phase alternative, filters 1 or 4 are the only choices and either would be fine.

I measured the Soul’s output with the digital filter switch in each mode, sharp and slow, using 2 test signals: a frequency sweep and a square wave. From this, I measured frequency and phase response, group delay and impulse response. Charts/graphs are below, in the appendix.

Here’s the square wave: first sharp, then slow:

Overall, at 44.1 kHz I observed 3 key differences:

  1. In sharp mode, frequency response and group delay are both flat to 20 kHz.
  2. In slow mode, frequency response starts to roll off and group delay starts to rise between 18 and 19 kHz.
  3. In slow mode, the square wave shows no ripple before a transition, and ripples with greater amplitude and longer duration after a transition.
  4. The above curves are similar when comparing the sharp & slow filters at 48k sampling.

From these observations I conclude that for 44.1k and 48k signals, the Soul uses filters 3 and 4 in sharp and slow modes, respectively. Here’s why:

  • Because FR is flat to 20 kHz in sharp mode, it must be using filter 1 or 3.
  • Because GD is flat in sharp mode, it must be using filter 3.
  • Because FR rolls off just above 18k in slow mode, it must be using filter 2, 4 or 5.
  • Because GD rises in slow mode, it must be using filter 4.

Appendix

I recorded these graphs using my sound card, an ESI Juli@. This is not a great setup, but it’s the best I can do without dedicated equipment.

PC USB Audio output –> Corda Soul USB input –> Corda Soul analog output –> sound card analog input

Details:

  • Configured the sound card for analog balanced input & output (flip its daughter board from unbalanced to balanced.
  • Cabled from Soul to Juli@, using 3-pin XLR to 1/4″ TRS.
  • On PC:
    • Disable pulseaudio
    • Use Room EQ Wizard (REQW) on PC, in ALSA mode
    • Configure REQW
      • set desired sampling rate (44.1, 48, 88.1, 96)
      • set audio output to USB
      • set audio input to Juli@ analog
    • Configure Corda Soul
      • Select USB audio input
      • Ensure all DSP disabled (knobs at 12:00)
      • Set volume as desired
        • measured at max: 0 dB
        • measured at 12:00; -16 dB; 34 clicks down
    • Use REQW “Measure” function
    • Confirm proper sampling rate light on Corda Soul

Important Note: My measurements depend as much on the Corda Soul as they do on the Juli@ sound card. For example, if the Juli@ rolls off the frequency response faster than the Soul, then I will measure the same FR in both mods of the Soul. And if the Juli@ applies a minimum phase filter that adds phase distortion, then I will measure that phase distortion in both modes of the Soul. This probably explains why the digital filter responses were so similar at 88 and 96 kHz.

Here are FR, phase, GD and impulse plots for all tested sampling rates. Each is sharp top, slow bottom. Observe that at multiples of 44.1k (44.1k and 88.2k), the sharp filter has flat phase response while the slow filter does not. But at multiples of 48k (48k and 96k), both filters have similar non-flat phase response. This is probably due to the Juli@ card. However, the comments below assume the Juli@ card is transparent and all differences are due to the Soul.

In all cases, both filters at all sampling rates:

  • Frequency response: starts to taper at 20 kHz for the widest possible transition band.
  • Impulse response: sharp is symmetric, slow is asymmetric.
  • Group delay: sharp is flatter than slow.
  • At high sampling rates, the difference between the filters becomes immaterial. This is consistent with theory.

44.1 kHz: sharp is filter 3 and slow is filter 4.

  • Sharp FR doesn’t taper until past 20k, so it must be filter 1 or 3.
  • Sharp has flat GD, so it must be filter 3.
  • Slow FR tapers past 19k, so it must be filter 4 or 5.
  • Slow has more GD than sharp, so it must be filter 4.

48 kHz: sharp is filter 3 and slow is filter 4, for the same reasons as above.

88.1 kHz: Sharp is filter 2 and slow is filter 1.

  • Both FR start to taper at 20 kHz, so neither can be filter 3.
  • Both have a stopband at 44,100 kHz (beyond 40k), so neither can be filter 4 or 5.
  • Sharp has flatter phase / less group delay, which is filter 2.

96 kHz: Sharp is filter 2 and slow is filter 1.

  • Both FR start to taper at 20 kHz, so neither can be filter 3.
  • Both have a stopband at 48 kHz (beyond 44k), so neither can be filter 4 or 5.
  • Sharp has flatter phase / less group delay, which is filter 2.

Blind Audio Testing: A/B and A/B/X

Blind Testing: Definitions

The goal of a blind audio test is to differentiate two sounds by listening alone with no other clues. Eliminating other clues ensures that any differences detected were due to sound alone and not to other factors.

A blind audio test (also called A/B) is one in which the person listening to the sounds A and B doesn’t know which is which. It may involve an person conducting the test who does know.

A double-blind audio test (also called A/B/X) is one in which neither the person listening, nor the person conducting the test, knows which is which.

In a blind test, it is possible for the test conductor to give clues or “tells” to the listener, whether directly or indirectly, knowingly or unknowingly. A double-blind test eliminates this possibility.

What is the Point?

The reason we do blind testing is because our listening/hearing perception is affected by other factors. Sighted listening, expectation bias, framing bias, etc. This is often subconscious. Blind testing eliminates these factors to tell us what we are actually hearing.

The goal of an A/B/X test is to differentiate two sounds by listening alone with no other clues. Key word: differentiate.

  • A blind test does not indicate preference.
  • A blind test does not indicate which is “better” or “worse”.

Most people — especially audio objectivists — would say that if you pass the test, then you can hear the difference between the sounds. And if don’t, then you can’t. Alas, it is not that simple.

  • If you pass the test, it doesn’t necessarily mean you can hear the difference.
  • If you fail the test, it doesn’t necessarily mean you can’t hear the difference.
  • If you can hear the difference, it doesn’t necessarily mean you’ll pass the test.
  • If you can’t hear the difference, it doesn’t necessarily mean you’ll fail the test.

Hearing is Unique

Hearing is quite different from touch or sight in an important way that is critical to blind audio testing. If I gave you two similar objects and asked you to tell whether they are exactly identical, you can perceive and compare them both simultaneously. That is, you can view or touch both of them at the same time. But not with sound! If I gave you two audio recordings, you can’t listen to both simultaneously. You have to alternate back and forth, listening to one, then the other. In each case, you compare what you are actually hearing now, with your memory of what you were hearing a moment ago.

In short: audio testing requires an act of memory. Comparing 2 objects by sight and touch can be done with direct perception alone. But comparing 2 sounds requires both perception and memory.

Audio objectivists raise a common objection: “But surely, this makes no difference. It only requires a few seconds of short-term memory, which is near perfect.” This sounds reasonable, but evidence proves it wrong. In A/B/X testing, sensitivity is critically dependent on fast switching. Switching delays as short as 1/10 second reduce sensitivity, meaning it masks differences that are reliably detected with instantaneous switching. This shows that our echoic memory is quite poor. Instantaneous switching improves sensitivity, but it still requires an act of memory because even with instant switching you are still comparing what you are actually hearing, with your memory of what you were hearing a moment before.

This leaves us with the conundrum that the perceptual acuity of our hearing, is better than our memory of it. We can’t always remember or articulate what we are hearing. Here, audio objectivists raise a common objection: “If you can’t articulate or remember the differences you hear, then how can they matter? They’re irrelevant.” Yet we know from numerous studies in psychology that perceptions we can’t articulate or remember can still affect us subconsciously — for example subliminal advertising. Thus it is plausible that we hear differences we can’t articulate or remember, and yet they still affect us.

If this seems overly abstract or metaphysical, relax. It plays no role in the rest of this discussion, which is about statistics and confidence.

Accuracy, Precision, Recall

More definitions:

A false positive means the test said the listener could tell them apart, but he actually could not (maybe he was guessing, or just got lucky). Also called a Type I error.

A false negative means the test said the listener could not tell them apart, but he actually can (maybe he got tired or distracted). Also called a Type II error.

Accuracy is what % of the trials the listener got right. An accurate test is one that is rarely wrong.

Precision is what % of the test positives are true positives. High precision means the test doesn’t generate false positives (or does so only rarely). Also called specificity.

Recall is what % of the true positives pass the test. High recall means the test doesn’t generate false negatives (or does so only rarely). Also called sensitivity.

With these definitions, we can see that a test having high accuracy can have low precision (all its errors are false positives) or low recall (all its errors are false negatives), or it can have balanced precision and recall (its errors are a mix of false positives & negatives).

A blind audio test is typically a series of trials, in each of which the listener differentiates two sounds, A and B. Given that he got K out of N trials correct, and each trial has 2 choices (X is A or X is B), what is the probability that he could get that many correct by random guessing? Confidence is the inverse of that probability. For example, if the likelihood of guessing is 5% then confidence is 95%.

Confidence Formula

p = probability to guess right (1/2 or 50%)
n = # of trials – total
k = # of trials – successful

The formula:

(n choose k) * p^k * (1-p)^(n-k)

This gives the probability that random guessing would get exactly K of N trials correct. But since p = 1/2, (1-p) also = 1/2. So the formula can be simplified:

(n choose k) * p^n

Now, substituting for (n choose k), we have:

(n! * p^n) / (k! * (n-k)!)

However, this formula doesn’t give the % likelihood to pass the test by guessing. To get that, we must add them up. For example, consider a test consisting of 8 trials using a decision threshold of 6 correct. To pass the test, one must get at least 6 right. That means scoring 6, 7 or 8. These scores are disjoint and mutually exclusive (each person gets a single score, so you can’t score both 6 and 7), so the probability of getting any of them is the sum of their individual probabilities.

Now you can do a little homework by plugging into this formula:

  • 4 trials all correct is 93.8% confidence.
  • 5 trials all correct is 96.9% confidence.
  • 7 correct out of 8 trials (1 mistake) is 96.5% confidence.

The Heisen-Sound Uncertainty Principle

A blind audio test cannot be high precision and high recall at the same time.

Proof: the tradeoff between precision & recall is defined by the test’s confidence threshold. Clearly, we always set that threshold greater than 50%, otherwise the results are no better than random guessing. But how much more than 50% should we set it?

At first, intuition says to set it as high as possible. 95% is often used to validate statistical studies in a variety of fields (P-test at 5%). From the above definitions, the test’s confidence percentile is its precision, so we have only 5% chance for a false positive. That means we are ignoring (considering invalid) all tests with scores below 95%. For example, somebody scoring 80% on the test is considered invalid; we assume he couldn’t hear the difference. But he did better than random guessing! That means he’s more likely than not to have heard a difference, but it didn’t reach our high threshold for confidence. So clearly, with a 95% threshold there will be some people who did hear a difference for whom our tests falsely says they didn’t. Put differently, at 95% (or higher) we are definitely going to get false negatives.

The only way to reduce these false negatives is to lower our confidence. The extreme case is to set confidence at 51% (or anything > 50%). Now we’ll give credit to the above fellow who scored 80% on the test. And a lot of other people. Yet this is our new problem. In reducing false negatives, we’ve increased false positives. Now someone who scores 51% on the test is considered valid, even though his score is low enough he could easily have been guessing.

The bottom line: the test will always have false positives and negatives. Reducing one increases the other.

Optimal Confidence

The ideal confidence threshold is whatever serves our test purposes. Higher is not always better. It depends on what we are testing, and why. Do we need high precision, or high recall? Two opposite extreme cases illustrate this:

High precision: 99% confidence
We want to know what audio artifacts are audible beyond any doubt.

Use case: We’re designing equipment to be as cheap as possible and don’t want to waste money making it more transparent than it has to be. It has to be at least good enough to eliminate the most obvious audible flaws and we’re willing to accept that it might not be entirely transparent to all listeners.

Use case: We’re debunking audio-fools and the burden of proof is on them to prove beyond any doubt that they really are hearing what they claim. We’re willing to accept that some might actually be hearing differences but can’t prove it (false negatives).

High recall: 51% confidence
We want to detect the minimum thresholds of hearing: what is the smallest difference that is likely to be audible?

Use case: We’re designing state-of-the-art equipment. We’re willing to over-engineer it if necessary to achieve that, but we don’t want to over-engineer it more than justified by testing probabilities.

Use case: Audio-fools are proving that they really can hear what they claim, and the burden of proof is on us to prove they can’t hear what they claim. We’re willing to accept that some might not actually be hearing the differences, as long as the probabilities are on their side however slightly (false positives).

Conclusion

To mis-quote Churchill, “Blind testing is the worst form of audio testing, except for all the others.” Blind testing is an essential tool for audio engineering from hardware to software and other applications. For just one example, it’s played a crucial role in developing high quality codecs delivering the highest possible perceptual audio quality with the least bandwidth.

But blind testing is not perfectly sensitive, nor specific. It is easy to do it wrong and invalidate the results (not level matching, not choosing appropriate source material, ignoring listener training & fatigue). Even when done right it always has false positives or false negatives, usually both. When performing blind testing we must keep our goals in mind to select appropriate confidence thresholds (higher is not always better). We should recognize its limitations and take them into account when interpreting the results. Most blind testing is done with a decision threshold of 95% confidence, which minimizes false positives yet increases false negatives, which means human hearing acuity is better than the test indicates.

Survey Bias

With Census 2020 coming around, the topic of survey bias will certainly arise. Drafting neutral surveys free of bias requires understanding in several disciplines from math, to language, psychology, demographics, and a quite a bit of experience & judgement. Here are some of the more obvious forms.

Sample Bias

People living in the same neighborhoods have some common demographics and common opinions on certain topics. This also applies in the virtual world: people who visit certain web sites (say, New York Times, Wired, and Wall Street Journal).

Sometimes sample bias can be unintentional and subtle. The people you surveyed had something in common that you didn’t know about.

Framing Effect Bias

People respond differently to questions depending on how you ask them or “frame” the question. This is one of the most important biases.

For example, 93% of students registered early when a late penalty fee was assessed. But only 67% registered early when the fee was called a discount for early registration.

Another example: suppose 600 people have a deadly disease. Treatment A is predicted to result in 400 deaths. Treatment B is 33% likely to have no deaths, but 67% likely for all 600 to die.

When framed positively: A saves 200 lives, and B has a 33% chance of saving all 600, and 67% chance to save nobody.
Here, A was preferred by 72% of people.

When framed negatively: With A, 400 people will die. B has a 33% chance that nobody will die, and a 67% chance that all 600 die.
Here, A was preferred by 22% of people.

In the long term, outcomes from A and B are the same. Yet how the question is framed made a huge difference in which people preferred.

Response (and non-Response) Bias

This is similar to sample bias. Different people have different rates of response to your survey. Here, you can get burned either way. If you sample every group at the same rate, the uneven response rates can bias your data. If you sample groups at different rates, you can introduce a new bias. Eliminating this kind of bias requires measuring the different response rates and carefully targeting your sampling.

Question Order Bias

The answers people give to early questions influence how they answer later questions. Thus, questions can be ordered to lead people to answer later questions in certain ways. In multiple choice surveys, this also applies to the order in which each question’s potential answers are provided.

Corda Jazz: Measurements

I own this headphone amp and use it every day at work. It has great sound quality with some unique features. I previously reviewed it and compared with other amps here.

Earlier this year I loaned this amp to Amir to measure for Audio Science Review, here. Amir does a great service to the audiophile community, I’ve met him in person and he’s a good guy with industry experience and a knowledgeable audiophile. However, we are all human with different opinions, and even objective measurements can be misleading.

Take SNR (signal:noise ratio) and SINAD (Signal over Noise and Distortion) for example. These are typically measured at a device’s full scale output, as this usually gives the highest number. But with headphone amps, we don’t listen at full volume. Their max output level is around 2-4 Vrms, sometimes more. This is far too loud for average listening levels; it would be painful or cause hearing damage. We typically listen with average levels around 70 or 80 dB SPL, which, perceptually, most people would describe as medium-loud. Most headphones reach this level with a voltage around 50 mV.

For example, consider the Matrix Audio Element, which Amir recently reviewed. It is one of the best DACs he’s ever measured, with a SINAD of 120 dB. However, its 50 mV SINAD is only 81 dB.

For comparison, The Corda Jazz measured about 87 dB SINAD at full output, and 90 dB at 50 mV output.

This illustrates an important point. We start with 2 devices. One has a SINAD of only 87 dB, which seems low. The other has a SINAD of 120 dB, which is the best he’s ever measured. Objective measurements tell us one is better! However, that is highly misleading because when you measure the output at levels we actually use, the exact opposite happens. The Jazz is actually 9 dB better than the Matrix. That’s a 65% drop in noise & distortion, which is a significant, audible improvement.

In short, the max SINAD measurement is correct, but misleading because it describes conditions that nobody actually uses when listening. The 50 mV SINAD is a better measurement because it represents actual listening conditions. But virtually nobody measures this; Amir (much to his credit) is the only person I know of who does this. Furthermore, the large variance between these two belies their similarity: as in the above example, the devices measuring the highest peak SINAD often do not measure the highest 50 mV SINAD, which proves how important it is to more closely scrutinize the measurements we make and their relevance to what we hear.

Enough said about this. Next I’ll talk about how the way an amp is designed affects this. If you don’t care about engineering details just skip to the conclusion.

Lesson learned: an amplifier’s SNR or SINAD can be quite different at 50 mV than it is at full output. How does this happen? The conventional amplifier has its internal gain-feedback loop set to whatever fixed gain ratio produces the desired maximum output, and the volume control is a potentiometer (variable resistor) that attenuates this. This “fixed gain with attenuation” means the noise level is relatively constant (based on the gain ratio, which is fixed), so as you turn the volume down, you reduce the SNR and SINAD at the same time.

This is easily seen with the Matrix. Full output is 3.9 V, so 50 mV is 38 dB quieter. And its 81 dB 50 mV SINAD is 39 dB less than 120 dB. What a coincidence: turn the volume down by 38 dB and SINAD drops by 39 dB! They have a virtually perfect 1:1 relationship. Not a coincidence; that’s by design.

So what’s happening with the Jazz? Its SINAD actually gets better at lower volumes. The Jazz is designed differently from typical amps. It does not use fixed gain with separate attenuation, but instead it uses variable gain to set the attenuation you need, obviating any need for separate attenuation.

The Jazz volume control changes the resistors in its internal gain-feedback loop. At low volumes, it has less gain and more negative feedback (wider bandwidth, lower noise and distortion). As you turn up the volume, you are increasing the gain (reducing negative feedback). [Incidentally, this means it must be inverting, for its gain-feedback loop to have less than unity gain. But its final fixed-gain stage is also inverting, so overall it does not invert.] Finally, this volume control is not a potentiometer; there is no potentiometer in the signal path.

This means the Jazz produces its best sound quality at the low to medium levels we actually use for listening. It also means the Jazz has perfect channel balance at every volume setting. Another observation from Amir’s measurements is that the Jazz is not current limited. It puts out 10x more power into 30 ohms, than 300 ohms.

Amir didn’t like the Jazz in his review, mainly because of its limited output power. One of the limitations of the Jazz’s unique volume control is that the resistors in the gain-feedback loop can only handle limited voltages. If you turn up the volume too high, it produces huge amounts of audible distortion due to input stage voltage clipping. The Jazz maximum output level before the onset of this clipping & distortion is about 3.7 V. That equates to 116 dB SPL with Sennheiser HD-580 and 120 dB on Audeze LCD-2. This is more than loud enough for me. Anyone listening louder risks damaging his hearing.

In summary, the Jazz is an amp that Amir’s measurements show has perfectly flat frequency response, perfect channel balance at all volume settings, less than 1 ohm output impedance, and SINAD among the best he’s ever measured, at actual listening levels (50 mV). Yet he doesn’t recommend this amp because of its limited output voltage. Amir is right that exceeding an amp’s power limits creates easily audible distortion, thus is the most  likely way listeners will hear distortion from an amp. However, if the limits are high it’s easy enough to stay below them.

Classical Music Streaming: Primephonic & Idagio

The Problem

Streaming classical music has 2 basic problems.

Note: I use the term “classical” in the most general sense, from ancient (pre-renaissance) to modern, including early music, baroque, classical, romantic, etc.

Metadata

ID3 has become the standard metadata for music, defining fields like title, artist, album, etc. This has an impedance mismatch with classical music. For example, if the Chicago Symphony is playing the Brahms violin concerto with conductor Reiner and soloist Heifetz, who is the artist? Brahms, Chicago Symphony, Reiner or Heifetz? What is the title? Violin Concerto in D Major, Opus 77, Chicago Symphony Live, or some nickname? If you search for this piece on streaming services like Spotify, Tidal, or Amazon, you will find all of the above, each individual recording having different metadata. Exacerbating this problem is the fact that every piece from every composer typically has tens if not hundreds of different recorded performances by different artists. This inconsistency makes it frustrating to find classical music.

Sound Quality

The sonic quality of the recording presents another problem. Most popular music is recorded with terrible sound quality: massive dynamic compression with clipping, and extreme amounts of EQ and other processing. They’re engineered to sound as loud as possible for radio, streaming and listening in noisy environments with crappy earbuds. This makes it easier for streaming, since the recording was already squashed to death by the studio during production, sound quality doesn’t matter because there’s nothing to preserve. However, sound quality matters with classical music. These recordings are made to a higher standard, having minimal studio processing, preserving dynamics and detail that lossy compression would destroy. This is important to reveal subtle variations in artistry, such as how a pianist voices chords, to a cello player’s bowing technique, to a flute player’s tone colors. This makes it harder to stream classical music.

So while there is plenty of classical music on standard streaming services, finding the piece you want, and the available recordings, is frustrating if not impossible. And when you finally do find it, listening to it through the streaming service’s lossy compression can be more disappointing than satisfying.

Thus it comes as no surprise that streaming accounts for only about 25% of classical music consumption, compared to 64% for the rest of the market.

The Solution

Even though classical makes up only about 3% of music sales, companies have formed to solve these problems. The 2 most popular are Idagio and Primephonic, and they address both of the above problems. I did not explore Naxos, because my experience owning about 100 of their CD recordings is that their sound quality (with a few notable exceptions) is second rate, and they only stream their own content, making great performances of the past inaccessible.

These classical music streaming services define and populate their own metadata customized for classical music, and they stream at lossless CD quality. This transforms the classical music streaming experience and has the potential to fundamentally change how music lovers experience classical music.

If that last statement sounds over the top, let me explain. With hundreds of composers, each writing hundreds of works, each having hundreds of recordings by different artists, each bringing something new to the artistic expression of the work, there is more classical music than any normal person can listen to in one lifetime. Of course, not all performances, nor all recordings, are equal. So music lovers have relied on reviewers to help sort through all of this. But reviewers and listeners are all people with different opinions. The work or recording a listener is interested in might not have been reviewed. When it has, a listener might find to his consternation that he disagrees with the reviewer. And many other works that a listener doesn’t even know about might be worth consideration. For decades, classical music listeners have relied on reviewers as gatekeepers and guides.

Streaming upends all of this by reducing to zero the marginal cost of the next recording you listen to. Browse the full catalog, using the classical music customized metadata to find works and performances in your area of interest. Take a chance on new works, recordings or artists, that the cost of individual CDs or downloads might have prevented you from listening to. Listen to everything and decide for yourself; the only constraint is your time. And, listen anywhere you are: home, work, in the car or wherever.

Furthermore, these streaming services cost less than a subscription to a classical music magazine like Grammophon or Fanfare. More on costs below.

Review

Idagio is a German company that’s about 4 years old. They are based in Berlin and their service became available in the USA about a year ago (September 2018).

Primephonic is a newcomer; their service started about a year ago (August 2018).

Both companies are staffed by a mix of musicians, musical scholars, agents and software engineers. They believe in what they’re doing and have the domain expertise to do it right.

I found many reviews of Idagio and Primephonic, but most were pretty shallow, as if the reviewers didn’t actually use the services in-depth on different devices and situations to discover their strengths & weaknesses. Since both services provide a 2-week free trial, I did this myself during a period where I did some business travel so I got their full experience from home, work, and traveling. Here is what I learned.

Getting Started

Both services offer a 14-day free trial. Primephonic is the quickest and easiest, since they don’t require a credit card. Just sign up with your email and it’s ready to go. Idagio requires a credit card to sign up for the trial, but they don’t bill anything to it until the 14th day.

Both services also let you sign up with a Facebook or Google account instead of using your email. I don’t do social media and prefer not to link online accounts, so I did not use this option.

Catalog

Their catalogs are roughly the same total size, and similar: both services had about 75% of the pieces I searched for, from early (pre-renaissance) music to modern. Where they differ, Primephonic has better coverage of early music, and less well known works and artists. Idagio has better coverage of baroque to modern classical music. For example, Idagio didn’t have some Piffaro (only 3 albums versus 6) and Joel Frederiksen early music albums that Primephonic had. Primephonic didn’t have Levin’s Mozart Requiem performance with the Violins du Roy, but Idagio did.

Some notable works were missing from both catalogs. Neither had anything from Jacqueline DuPre, nor did either have the Hillier Ensemble’s Age of Cathedrals (this is just one of several albums I have that was not in the catalogs of either service).

Metadata and Search

They both have metadata customized for classical music. You can search by any keyword, from composer to work to group, to album. And the search results are cross-referenced, so if you find a work, for example, you can click on it to see all other works from that composer, or all albums having that work.

I found their metadata doesn’t have much information about the album. For example if I search for “Liszt Transcendental Etudes”, they both show a list of albums. If I click on one, say Berezovsky (available in both), it shows me a picture of the album cover and says, “1996 Teldec Classics”. But there is no catalog number or other recording info, not to mention liner notes.

Primephonic’s search may not be quite as robust as Idagio. I searched for the Brahms Piano Quintet Op 34 in both. Idagio showed several recordings of it. It did not appear in Primephonic at all, as if they didn’t have this popular work in their catalog. When I mentioned this to Primephonic support, they sent me a link to the piece — so they do indeed have it, but it wasn’t coming back in search results.

Music Discover-Ability

Despite this Primephonic glitch, in the Android app, their search is better than Idago’s. This is best explained by example. Suppose you want to find recordings of Liszt’s Transcendental Etudes.

In Primephonic: search for Liszt, tap him in results, and it shows a list of popular works. Tap Show All, but this list is too long to bother scrolling through, and you’re not sure whether it will appear under E for Etudes or T for Transcendental. The app has a Sort By box, enabling you to sort by Opus number, then you scroll to 139. Tap this, and it shows you 83 recordings which you can sort by popularity, A-Z, Z-A, newest, oldest, longest or shortest.

In Idagio: search for Liszt, tap him in results, and it shows 3 tabs: Works, Recordings, Albums. The Works tab has no way to sort or sub-search, it’s unclear how it’s sorted, and the list is too long to scroll, so that’s not helpful. The Recordings tab can sort by Date, Most Popular, or Recently Added, none of which help you find the Transcendental Etudes, so that’s not helpful. The Albums tab can sort by year or alphabetically, so this is not helpful either.

In short: Idagios’s Android app lacks sub-search or sort, making it more difficult to find the pieces you’re looking for. It’s easier to find things in the Primephonic app.

However, Idagio’s web browser does better than their app. Here, when you tap Liszt, Works can be grouped by Keyboard, Secular, Chamber, etc. This makes it easier to find stuff, but sort is still only by popularity or alphabet, so it’s still not as good as Primephonic.

Applications / Players

Both services are fully functional in a web browser, and in Android and iOS apps that are free to install (not including the subscription price) from the standard app stores. By fully functional I mean you can search the catalog and play music. I ran both services on my Browser (Chrome & Firefox on Ubuntu 16 and 18), phone (Galaxy Note 4 SM-N910T running LineageOS 16 / Android 9) and my tablet (Galaxy Tab S SM-T700 running LineageOS 14 / Android 7).

Primephonic audio had brief gaps or glitches every 10 seconds or so when playing from Firefox on my laptop (which makes listening impossible), but this didn’t happen from Chrome on the same laptop, nor did it happen in Firefox on my desktop. So this problem was probably Firefox, not Primephonic. Audio from both apps was seamless on my phone & tablet.

Idagio is more reliable with faster, smoother performance in both the browser and the Android app. Primephonic occasionally hung (both the app, and the web page) and had to be restarted or reloaded, which Idagio never did. Also, Primephonic had a bug in which the app’s streaming quality settings don’t appear to be saved, but revert to the defaults every time I saw them, even after I changed them.

The Primephonic app supports both portrait & landscape mode, which makes it easier to use on my tablet. This is a nice little touch compared to Idagio’s app, which is always in portrait mode, even on the tablet.

Both apps enable you to download tracks or entire albums to your device so you can play them back anytime, even when disconnected. This was great on a cross-country flight. However, neither app supports external SD cards, so whatever you download consumes internal storage. When downloading, Idagio’s app creates an Android notification with a progress bar, and it also indicates in your music library the pending download status. Primephonic’s download is more of a black box – it doesn’t have a notification and you’re never sure exactly when it’s downloading, or when it might finish. But it does mark which tracks or albums in your library are downloaded, when complete.

Both apps stream smoothly and seamlessly, whether live streaming or playing pre-downloaded content, listening on headphones plugged into the device, or over bluetooth in my car. And my car’s audio next/previous track controls also worked when playing music from the apps on my phone.

Sound Quality

Both support CD quality streaming as FLAC, which uses lossless compression. Listening to them on my audio system, the sound quality of both services was as good (or bad) as the recordings themselves on CD. To test this, I configured each service to stream in CD quality, then found CDs in my collection in each service, and streamed it with the CD playing, and quick switching back and forth I found them indistinguishable. My audio system is quite transparent and I can distinguish 320 kbps MP3 from CDs in blind listening tests, so this test suggests that each service is streaming the audio stream as-is, without processing it.

Both services also support lower quality (lossy compression) streaming to reduce data usage, which is useful for phones. These still offer good sound quality (192-320 kbps) that exceeds most other music streaming services.

Primephonic has settings for different rates on mobile versus Wifi data, which is useful and distinguishes it from Idagio, which just has a single quality setting.

Primephonic has gapless playback, but Idagio does not. Frequently, classical tracks or movements blend right into each other without any break in the music. Without gapless playback, the audio system inserts a break. This could be an important consideration for some listeners.

Data Consumption

I mentioned that both apps can stream audio at true CD quality, yet they also provide lossy compression to save mobile data. This is especially useful because when listening on your phone, you’re often in a situation where reference quality audio isn’t needed: in the car or other noisy environment, using BlueTooth audio or earbuds plugged into your phone. Even some of the best IEMs and earbuds don’t have the same reference audio quality as full size headphones or listening rooms. So CD quality streaming only wastes mobile data when you can’t hear the difference.

I measured the actual data usage by each app when streaming audio over my mobile connection.

Before getting into the differences, here is approximate expected data usage per hour at a few standard music streaming rates:

  • 128 kbps = 1 MB / minute, 60 MB / hour
  • 320 kbps = 2.4 MB / minute, 144 MB / hour
  • CD (44 k / 16 b uncompressed)  = 1,411 kbps = 10.5 MB / minute, 640 MB / hour
  • CD FLAC (lossless compression) = 6 MB / minute, 400 MB / hour

Primephonic

Offers 4 quality settings: Normal (128 kbps), High (256 kbps), Superior (320 kbps), Full (lossless up to 24-bit). Also, allows different settings on WiFi versus mobile, which is quite useful.

However, when streaming music, Primephonic consumed about 200 MB per hour regardless of the setting. That is higher than 320 kbps.

This is a bug in their Android app that makes it essentially unusable for streaming over mobile.

Idagio

Offers 3 quality settings: Normal (AAC 192 kbps), High (MP3 320 kbps), Lossless (FLAC of 1411 kbps). This is a single global setting whether on WiFi or mobile. It also offers a quality setting for downloads: Normal (750 Kb per minute, about 128 kbps), High (2.5 Mb per minute, about 320 kbps), or Lossless (up to 10 Mb per minute, but about 2/3 of that due to lossless compression).

When streaming music, Idagio consumed about 80 MB per hour at Normal and 200 MB per hour at High.

Customer Support

I emailed support for both services with various bug reports & suggestions. Both responded to all my emails, and not robotically but from an actual human who understood my message and gave a courteous, intelligent response. Primephonic was a bit faster, responding in less than 24 hours even on weekends. Idagio took a couple of days to respond, which is still quite good.

Cost

Their cost is similar but not the same. Idagio is simple with a single service tier: $10 / month. No discount for buying a year up front, so it’s $120 / year.

Primephonic has tiered service depending on the streaming audio quality. It costs $8 / month for up to 320kbps lossy, and $15 / month for CD quality or higher. Primephonic has discounts for buying a year up front, which costs $80 and $150 respectively.

So, Primephonic can be cheaper or more expensive than Idagio, depending on whether you want full CD quality streaming.

Artist Reimbursement

Both services reimburse performers differently from other streaming services, in a way that is better suited to classical music, where track lengths vary tremendously. Reimbursing by track play starts just doesn’t make sense. Instead, they reimburse performers based on the time individual subscribers spend listening to specific tracks. In short, reimbursement is based on time spent, not starts.

Conclusion

To say that I’ve enjoyed these trials would be an understatement. It’s wonderful to have such a huge library of classical music at my disposal to listen wherever I want, at home, at work, in my car, or while traveling. Also, each service has curated lists of music in different areas of interest, which can be a useful exploration guide.

I like early music so I lean toward Primephonic due to their slightly better coverage, gapless playback, and their slightly better music search & discover-ability. However, the fact that their Android app always consumes 200 MB / hour when streaming is a show-stopper. And they’re more expensive, at least for full CD quality, and their app is a little more buggy.

I’m definitely going to subscribe to one of these services, but I still haven’t decided which one. They’re quite similar, each has its minor differences, pro & con, and neither is clearly better. I hope this detailed review has helped you decide whether you want a service like this and which might be best for you.