Category: hearing

Typical Errors in Digital Audio: Part 5 – What time is it there?

I’m originally from Newfoundland – one of the few places in the world with a 1/2-hour time zone. So, when it’s 10:00 a.m. in Montreal, it’s 11:30 a.m. in St. John’s – my home town. This meant that, when I was a kid 40 years ago, and we would call our relatives in Toronto or Germany to wish them a Merry Christmas, there were two questions that you could always rely on being asked: (1) what’s the weather like there? and (2) what time is it there?

These days, I have a similar problem that is well-described by “Segal’s Law“. My iPhone and my wristwatch (an old analogue one with hands that go around pointing at the floor and the fridge…) are never synchronised… This is because of two things: (1) I probably did a bad job of setting my watch and (more importantly) (2) my watch runs just a little bit slowly…

So, let’s say, for example, that I set my watch to be EXACTLY in sync with my phone on a Monday morning at 9:00 a.m. As the week goes by, my iPhone and my watch drift apart, and, just for the sake of argument let’s say that, one week later, when my iPhone turns over to 9:00 a.m. on Monday morning, my wristwatch turns over to 8:59 a.m. So, I lose 1 minute per week on my watch.

(It’s pretty safe to assume that my iPhone is also not perfect – but it’s different because, every once in a while, it compares its internal clock with another, more accurate clock somewhere else via a connection across the Internet (which, we will assume, for the purposes of this discussion, works).)

Let’s consider this from a strange point of view. Let’s assume that

  • I’m checking the time on my watch every minute, on the minute
  • someone else is “fixing” my watch every week so that it’s correct at 9:00 a.m. on Mondays. They do this by adjusting the watch to the correct time 30 seconds before the iPhone says it’s 9:00 a.m.
  • I don’t know that they’re doing this for me…

If we think about this from my perspective, I’ll live in a strange world where 8:59 on Mondays never exists. This is because at 8:58 and 30 seconds (on my watch), my friend re-sets the time to 8:59 and 30 seconds (while I’m not looking) to synchronise with the iPhone…

 

IF my watch was running fast – say, gaining one minute each week, then I would live in a different strange universe where 9:00 happens twice every Monday morning…

 

The basic problem here is that we have two clocks that do not run at the same rate – but they are expected to do so. So, we synchronise them regularly (in the above example, on Monday mornings at 9:00) – but between those synchronisation events, they drift apart in time.

 

So what?

The example above is very, very similar to the way a digital audio streaming system works – especially if you’re using a wireless connection between the transmitting device and a receiver.

Lets say that you’re playing a sound file that was recorded at 44.1 kHz and streaming it wirelessly to a receiver. I’m trying to be as generic as possible here, but I could be talking about a Bluetooth connection to a pair of headphones or a WiFi connection via DLNA to a device connected to a pair of loudspeakers, for example…

It is not unusual with such a connection for the transmitter to collect up a block of audio samples – say, 64 of them – and send them to the receiver’s input buffer. The receiver then pulls those samples out, one by one, and (eventually) sends them to a digital-to-analogue converter that produces a signal that (eventually) comes out as an audio signal. Then, 64/44100’ths of a second later (64 samples later) the transmitter sends another block, and so on and so on until the song ends.

This system works well if the clock inside the transmitter and the clock inside the receiver are perfectly synchronised. We can even be a little generous and say that they can drift apart a little – but not so much that we either run out of samples to play (because the receiver is playing them out faster than they’re coming in from the transmitter) or that we have samples left over to play when the next block comes in (because the receiver is playing them out slower than they’re coming in from the transmitter).

 

Dealing with this problem the right way

The right way to deal with this issue is for the receiver to always be checking what time it thinks it is when the block arrives from the transmitter. If the block arrives a little early, then the receiver should think “hmmmm, my clock is going too slowly – I’ll speed it up a bit”. If the block arrives a little late, then the receiver should adjust its clock to go a little slower.

So, in this case, the receiver has a basic, nominal speed for its internal clock – but it’s constantly adjusting it to be faster and slower to try and match the clock of the transmitter – but it can only do this adjustment at the block rate – the frequency at which the blocks of samples arrive, which is dependent on the block length (how many samples are in each block) and the sampling rate (how many samples per second). (Of course, this can result in “jitter and wander” problems if you’re not careful (I won’t talk about this here…) – so you have to pay a little attention to how quickly you’re adjusting your clock rate… but that’s “just” a matter of correct implementation.)

 

Dealing with this problem the wrong way

There is another way to deal with this problem, which, unfortunately, has measurable and possibly audible consequences. This implementation is basically the same as my original example, where I had a friend “fixing” my wristwatch once a week. You have a transmitter that sends blocks of samples to the receiver – and although these two devices should have exactly the same clock rate, they don’t.

Let’s say, for example, that the receiver is playing the samples faster than they’re being sent by the transmitter. This means that the two will slowly drift farther and farther apart until, eventually, the receiver will have to play a sample, but nothing has come in from the transmitter yet, so there’s no sample there to play. In this case, the receiver says “no problem, I’ll just play the last sample again, and the next block will come in while I’m doing that” – so it inserts an extra sample that is just a duplicate of the previous one.

If the receiver’s clock is going slower than the transmitter’s, then, as the two drift farther apart, we will get to a moment where the receiver will receive a new block of samples but it’s not done playing all of the samples in the previous block yet. In this event, it says “no problem, I’ll just leave that last sample out and move on to the next block to catch up” – so it skips a sample.

This is called a “Skip / Insert”  strategy for dealing with clock synchronisation. It’s done by software and hardware engineers because it’s simple to implement, and, in many cases, a manufacturer can get away with this, since it is rarely audible for a couple of reasons.

Can this be measured?

The simple answer to this is “yes” – and it can be measured in a number of different ways. I’ll show one way below…

Can I hear it?

The honest answer to this question is “sometimes” – but it’s not as easy to detect as one might think. Of course, a skip/insert event (a duplicated sample or a dropped one) creates an artefact. However, the magnitude of this artefact relative to the “correct” signal is dependent on when it happens.

Let’s take a look at a couple of simple cases. We’ll “transmit” one period of a sine wave that should come out on the other side of the system looking like Figure 1.

Fig 1: The original signal that we want to transmit

But what happens if we don’t get a block in time to keep outputting a signal? We insert a duplicate sample and hope that the block comes in before I have to send out another one. Examples of this are shown in Figures 2 and 3, below.

Fig 2: Insert example 1
Fig 3: Insert example 2

You’ll probably notice that it’s much easier to see which sample I duplicated in Figure 3 than in Figure 2. In Figure 3 it was sample number 26 that was duplicated. In Figure 2 it’s sample number 13.

The reason it’s easier to see the error in Figure 3 is that duplicating the sample causes an obvious change in the slope of the signal, whereas in Figure 2 it does not – the slope of the signal is 0, and by duplicating a sample, I am also making it 0 – but for a slightly longer time.

This does not mean that we did not generate an error. It just means that we’ll probably “get away with it” in the case of Figure 2, and we probably won’t in the case of Figure 3.

However, since the drifting of the two clocks (in the receiver and transmitter) are not dependent on the signal, there’s no way to know when this is going to happen.

And, of course, if this happens in the middle of a snare drum hit or a ssssinger sssstarting a word in a ssssong with the letter “s” – then we also won’t hear it because there’s so much going on (frequency-wise) that the artefact will be buried in the mess.

Also, since this clock drifting is usually not completely regular, the errors do not usually come in at a regular rate (although I’ve seen exceptions…). So, it’s not like you can listen for “a click every second” or “one per minute”. They happen when they happen – hopefully when you’re not listening and/or when the tune is busy enough to hide it.

 

A skip event is similar to an insert, as you can see in the two examples in Figures 4 and 5.

Fig 4: Skip example 1
Fig 5: Skip example 2

Again, I’ve intentionally put in these two skips in places where they are least obvious (Figure 4) and most obvious (Figure 5).

 

The real world

One of the tests that can be done on an audio system is to send a sinusoidal signal with a swept frequency through a system, capture the output, and then do a spectrogram of the result. In theory, if you see anything other than a single frequency at any one time at the output, then you know that something has happened to the signal. You would probably then need to go back and look at the output signal itself to start evaluating exactly what happened… This is a test that is used to evaluate one aspect of the performance of different sampling rate converters, for example, at this site.

Let’s take a sine sweep and run it through a system. The sweep goes up logarithmically in frequency from 20 Hz to about 90% of Nyquist (which would correspond to 20,000 Hz in a system running at 44.1 kHz) over 60 seconds and has a level of -1 dB FS. We’ll then capture the output in a system that is behaving perfectly and do a spectrogram of this, looking for artefacts down to some level below the signal level. (If you’re really geeky, you’ll know that this signal-to-error ratio is dependent on the window length of the FFT I’m using to create the spectrogram – but this is beyond our discussion today…).

An example of the output of a system that is behaving well is shown in Figure 6.

 

Fig 6. A spectrogram of a sinusoidal signal, swept in frequency over 60 seconds. Notice that there are no additional signals within 50 dB (the scale on the right) of the signal.

You may notice that the plot looks a little “wide” in the beginning. This is because the window length of the FFT I’m using to analyse the signal isn’t long enough to get a precise analysis of a low-frequency signal. So, this is an artefact of the analysis – not an error in the playback system.

What happens if we have random skip/insert events in the system? This is shown in Figure 7.

Fig 7. Intentionally-created skip/insert events seen as artefacts in the frequency domain.

The signal in Figure 7 was one that I created – I intentionally made skip/insert events at random times and applied them to my test signal.

There are two things to notice here. The first is that each event is visible as a vertical “spike” in the plot. This is because a skip/insert event will cause a short, wide-band “burst” that sounds like a click. However, the bandwidth of the click is dependent on when it happens relative to the signal. For example, the skip/insert events in Figure 2 and 4 would not create as much high-frequency energy as the ones in Figure 3 and 5. So, the bigger the effect on the slope of the signal, the more high frequency energy we’ll get in our “click” sound. Since the slope of a signal increases with frequency, then this also means that low-frequency signals will likely produce lower-bandwidth artefacts.

Now let’s look at the results from some real-world devices and systems that are commercially available.

Fig 8. The same test run on a commercially-available system/device. If you’re curious about some of the information listed in the plot, you can decode it as follows: The title “44k1_16_-1dBFS_chan1_100dB_snr” means that the original file I was playing was a 44.1k kHz / 16 bit file. The level of the sinusoidal sweep was -1 dB FS, and TPFD dithered. The analysis we’re looking at here is for channel 1 (the left channel), and we’re looking for artefacts down to 100 dB below the signal level. The “96000” you see on the top left of the plot indicates that the output of the system was captured at a sampling rate of 96 kHz (the internal sampling rate of the sound card that I used to do this measurement).

 

As you can see in Figure 8, there was one skip/insert event that happened during the 60 seconds I was running this test. Remember that the time that that event happened had nothing to do with the frequency it was playing. It just happens when it happens due to the relationship between the transmitter’s and the receiver’s clock speeds.

 

Fig 9. Another commercially-available system/device.

 

Figure 9 shows the results from a different system/device that obviously uses a skip/insert strategy to deal with clock synchronisation problems. It also obviously has some serious clock issues, since it has to correct on the order of approximately once a second…

 

Fig 10. Another commercially-available system/device.

Figure 10 shows the results from a different system/device that uses a skip/insert strategy – but appears to do so at scheduled intervals. In this case, there is a high probability of getting a skip/insert event every 10 seconds with the counter starting at the instant I starting hearing the music.

 

Addendum 1

Inquisitive readers may be asking why it is that, although I’m doing an analysis down to -101 dB FS (100 dB below the signal level of -1 dB FS), you can’t see the effects of the dither noise floor in my original 16-bit file (which is normally assumed to be at -93 dB FS). This is because the -93 dB FS estimate of a dither signal assumes that you are looking at the total energy from the entire frequency band. The spectrograms above are based on FFT’s that split up the total frequency band into “slices” (called frequency bins) – and the total energy in each of these bins is less than the total energy in all of them (one person clapping is not as loud as 1000 people clapping at the same time…). If we wanted to see the dither noise, I would have had to set my analysis to go down approximately 30 dB lower – but the actual value for this is dependent on the relationship between the sampling rate, the window length of the FFT’s, and the windowing function that I’m using.

 

Addendum 2

Do not bother contacting me to ask which “commercially-available system/device” I measured and in which I found these errors. I’m not doing this to get anyone in trouble. I’m just doing this to try to illustrate common errors that I see often when I evaluate and test audio devices.

An besides, it would not be fair for me to rat on specific companies, systems, or devices, since, in some cases, these errors may have already been fixed with a firmware update, meaning that “naming names” would be irrelevant and unnecessarily detrimental.

But, I will say that I see this problem often. A rough estimate is that I would see errors like this on roughly half of the commercially-available devices and systems I test. It can also be sneaky, as we saw in Figures 8 and 10. Sometimes you get one of these clicks only once in a minute. So, if you do a 10-second measurement to test if your wireless audio receiver is “bit accurate” – the answer can be “yes” – but if you keep measuring for 1 or 2 minutes, you find out the answer is “no”…

 

Addendum 3

If it helps, I could have used the example of a leap year instead of two clocks at the beginning. The reason we have a February 29 every 4 years is that our calendar “runs” a little faster than the time it takes us to get around the sun (because a “year” is actually 365.25 days long…). So, every 4 years we have to “insert” a day to put the two clocks back in sync.

Also, since a “year” is not exactly 365.25 days long, we also have the occasional “leap second” as well. But most people don’t notice this, since it’s rarely useful as an excuse when you’ve missed a meeting…

Typical Errors in Digital Audio: Part 3 – Aliasing

Reminder: This is still just the lead-up to the real topic of this series. However, we have to get some basics out of the way first…

In the first posting in this series, I talked about digital audio (more accurately, Linear Pulse Code Modulation or LPCM digital audio) is basically just a string of stored measurements of the electrical voltage that is analogous to the audio signal, which is a change in pressure over time… In the second posting in the series, we looked at a “trick” for dealing with the issue of quantisation (the fact that we have a limited resolution for measuring the amplitude of the audio signal). This trick is to add dither (a fancy word for “noise”) to the signal before we quantise it in order to randomise the error and turn it into noise instead of distortion.

In this posting, we’ll look at some of the problems incurred by the way we carve up time into discrete moments when we grab those samples.

Let’s make a wheel that has one spoke. We’ll rotate it at some speed, and make a film of it turning. We can define the rotational speed in RPM – rotations per minute, but this is not very useful. In this case, what’s more useful is to measure the wheel rotation speed in degrees per frame of the film.

 

Fig 1. The position of a clockwise-rotating wheel (with only one spoke) for 9 frames of a film. Each column shows a different rotational speed of the wheel. The far left column is the slowest rate of rotation. The far right column is the fastest rate of rotation. Red wheels show the frame in which the sequence starts repeating.

 

Take a look at the left-most column in Figure 1. This shows the wheel rotating 45º each frame. If we play back these frames, the wheel will look like it’s rotating 45º per frame. So, the playback of the wheel rotating looks the same as it does in real life.

This is more or less the same for the next two columns, showing rotational speeds of 90º and 135º per frame.

However, things change dramatically when we look at the next column – the wheel rotating at 180º per frame. Think about what this would look like if we played this movie (assuming that the frame rate is pretty fast – fast enough that we don’t see things blinking…) Instead of seeing a rotating wheel with only one spoke, we would see a wheel that’s not rotating – and with two spokes.

This is important, so let’s think about this some more. This means that, because we are cutting time into discrete moments (each frame is a “slice” of time) and at a regular rate (I’m assuming here that the frame rate of the film does not vary), then the movement of the wheel is recorded (since our 1 spoke turns into 2) but the direction of movement does not. (We don’t know whether the wheel is rotating clockwise or counter-clockwise. Both directions of rotation would result in the same film…)

Now, let’s move over one more column – where the wheel is rotating at 225º per frame. In this case, if we look at the film, it appears that the wheel is back to having only one spoke again – but it will appear to be rotating backwards at a rate of 135º per frame. So, although the wheel is rotating clockwise, the film shows it rotating counter-clockwise at a different (slower) speed. This is an effect that you’ve probably seen many times in films and on TV. What may come as a surprise is that this never happens in “real life” unless you’re in a place where the lights are flickering at a constant rate (as in the case of fluorescent or some LED lights, for example).

Again, we have to consider the fact that if the wheel actually were rotating counter-clockwise at 135º per frame, we would get exactly the same thing on the frames of the film as when the wheel if rotating clockwise at 225º per frame. These two events in real life will result in identical photos in the film. This is important – so if it didn’t make sense, read it again.

This means that, if all you know is what’s on the film, you cannot determine whether the wheel was going clockwise at 225º per frame, or counter-clockwise at 135º per frame. Both of these conclusions are valid interpretations of the “data” (the film). (Of course, there are more – the wheel could have rotated clockwise by 360º+225º = 585º or counter-clockwise by 360º+135º = 495º, for example…)

Since these two interpretations of reality are equally valid, we call the one we know is wrong an alias of the correct answer. If I say “The Big Apple”, most people will know that this is the same as saying “New York City” – it’s an alias that can be interpreted to mean the same thing.

Wheels and Slinkies

We people in audio commit many sins. One of them is that, every time we draw a plot of anything called “audio” we start out by drawing a sine wave. (A similar sin is committed by musicians who, at the first opportunity to play a grand piano, will play a middle-C, as if there were other notes in the world.) The question is: what, exactly, is a sine wave?

Get a Slinky – or if you don’t want to spend money on a brand name, get a spring. Look at it from one end, and you’ll see that it’s a circle, as can be (sort of) seen in Figure 2.

Fig 2. A Slinky, seen from one end. If I had really lined things up, this would just look like a shiny circle.

Since this is a circle, we can put marks on the Slinky at various amounts of rotation, as in Figure 3.

Fig 3. The same Slinky, marked in increasing angles of 45º.

Of course, I could have put the 0º marl anywhere. I could have also rotated counter-clockwise instead of clockwise. But since both of these are arbitrary choices, I’m not going to debate either one.

Now, let’s rotate the Slinky so that we’re looking at from the side. We’ll stretch it out a little too…

Fig 4. The same Slinky, stretched a little, and viewed from the side.

Let’s do that some more…

Fig 5. The same Slinky, stretched more, and viewed from the “side” (in a direction perpendicular to the axis of the rotation).

When you do this, and you look at the Slinky directly from one side, you are able to see the vertical change of the spring from the centre as a result of the change in rotation. For example, we can see in Figure 6 that, if you mark the 45º rotation point in this view, the distance from the centre of the spring is 71% of the maximum height of the spring (at 90º).

Fig 6. The same markings shown in Figure 3, when looking at the Slinky from the side. Note that, if we didn’t have the advantage of a little perspective (and a spring made of flat metal), we would not know whether the 0º point was closer or further away from us than the 180º point. In other words, we wouldn’t know if the Slinky was rotating clockwise or counter-clockwise.

So what? Well, basically, the “punch line” here is that a sine wave is actually a “side view” of a rotation. So, Figure 7, shows a measurement – a capture – of the amplitude of the signal every 45º.

Fig 7. Each measurement (a black “lollipop”) is a measurement of the vertical change of the signal as a result of rotating 45º.

Since we can now think of a sine wave as a rotation of a circle viewed from the side, it should be just a small leap to see that Figure 7 and the left-most column of Figure 1 are basically identical.

Let’s make audio equivalents of the different columns in Figure 1.

Fig 8. A sampled cosine wave where the frequency of the signal is equivalent to 90º per sample period. This is identical to the “90º per frame” column in Figure 1.
Fig 9. A sampled cosine wave where the frequency of the signal is equivalent to 135º per sample period. This is identical to the “135º per frame” column in Figure 1.
Fig 10. A sampled cosine wave where the frequency of the signal is equivalent to 180º per sample period. This is identical to the “180º per frame” column in Figure 1.

Figure 10 is an important one. Notice that we have a case here where there are exactly 2 samples per period of the cosine wave. This means that our sampling frequency (the number of samples we make per second) is exactly one-half of the frequency of the signal. If the signal gets any higher in frequency than this, then we will be making fewer than 2 samples per period. And, as we saw in Figure 1, this is where things start to go haywire.

Fig 11. A sampled cosine wave where the frequency of the signal is equivalent to 225º per sample period. This is identical to the “225º per frame” column in Figure 1.

Figure 11 shows the equivalent audio case to the “225º per frame” column in Figure 1. When we were talking about rotating wheels, we saw that this resulted in a film that looked like the wheel was rotating backwards at the wrong speed. The audio equivalent of this “wrong speed” is “a different frequency” – the alias of the actual frequency. However, we have to remember that both the correct frequency and the alias are valid answers – so, in fact, both frequencies (or, more accurately, all of the frequencies) exist in the signal.

So, we could take Fig 11, look at the samples (the black lollipops) and figure out what other frequency fits these. That’s shown in Figure 12.

Fig 12. The red signal and the black samples of it are the same as was shown in Figure 11. However, another frequency (the blue signal) also fits those samples. So, both the red signal and the blue signal exist in our system.

Moving up in frequency one more step, we get to the right-hand column in Figure 1, whose equivalent, including the aliased signal, are shown in Figure 13.

Fig 13. A signal (the red curve) that has a frequency equivalent to 280º of rotation per sample, its samples (the black lollipops) and the aliased additional signal that results (the blue curve).

 

 

Do I need to worry yet?

Hopefully, now, you can see that an LPCM system has a limit with respect to the maximum frequency that it can deal with appropriately. Specifically, the signal that you are trying to capture CANNOT exceed one-half of the sampling rate. So, if you are recording a CD, which has a sampling rate of 44,100 samples per second (or 44.1 kHz) then you CANNOT have any audio signals in that system that are higher than 22,050 Hz.

That limit is commonly known as the “Nyquist frequency“, named after Harry Nyquist – one of the persons who figured out that this limit exists.

In theory, this is always true. So, when someone did the recording destined for the CD, they made sure that the signal went through a low-pass filter that eliminated all signals above the Nyquist frequency.

In practice, however, there are many cases where aliasing occurs in digital audio systems because someone wasn’t paying enough attention to what was happening “under the hood” in the signal processing of an audio device. This will come up later.

 

Two more details to remember…

There’s an easy way to predict the output of a system that’s suffering from aliasing if your input is sinusoidal (and therefore contains only one frequency). The frequency of the output signal will be the same distance from the Nyquist frequency as the frequency if the input signal. In other words, the Nyquist frequency is like a “mirror” that “reflects” the frequency of the input signal to another frequency below Nyquist.

This can be easily seen in the upper plot of Figure 14. The distance from the Input signal and the Nyquist is the same as the distance between the output signal and the Nyquist.

Also, since that Nyquist frequency acts as a mirror, then the Input and output signal’s frequencies will move in opposite directions (this point will help later).

 

Fig 14. Two plots showing the same information about an Input Signal above the Nyquist frequency and the output alias signal. Notice that, in the linear plot on top, it’s easier to see that the Nyquist frequency is the mirror point at the centre of the frequencies of the Input and Output signals.

 

Usually, frequency-domain plots are done on a logarithmic scale, because this is more intuitive for we humans who hear logarithmically. (For example, we hear two consecutive octaves on a piano as having the same “interval” or “width”. We don’t hear the width of the upper octave as being twice as wide, like a measurement system does. that’s why music notation does not get wider on the top, with a really tall treble clef.) This means that it’s not as obvious that the Nyquist frequency is in the centre of the frequencies of the input signal and its alias below Nyquist.