First steps towards a tuner
.pdf version
Abstract
A really good tuner shows the user how to tune by being
intuitive. That's a technical requirement towards which this
explanation goes. Some basics are touched and built on the way;
it would't bother if you have already written „hello world“.
„[...] it's just a little coding [...]“
„[...] it's not just the fft [...]“
Fork
[/
// initialize portaudio
err = Pa_Initialize();
if( err != paNoError ) goto error;
/]
1 Why?
Being a guitarist for 30 years, I encountered many ways to get
the Instrument in tune. When looking back I would like to know
how long my guitar was out of tune with me not knowing better...
tuning is daily work and not trivial for the beginner. My first
tuning tool was a thing to whistle about, which had six notes
corresponding to the strings. The sound of this was as far from a
guitar as could be, so the difference in pitch was hard to tell.
Next came the fifth fret method, as I had suffered long enough
from the whistle. A difficult task to handle, using two hands
crossed getting the strings to sound while you adjust the string.
I found it a little easier to get a glimpse on the difference in
pitch with this method, but nevertheless your hearing should be
trained to this. Depending on the guitar, this can be error prone
due to imperfect fretting. As playing evolves, overtones can be
used for tuning. I finally managed to get the guitar in tune
fairly well with overtones and a tuning fork. By this method it
became clear that no guitar can ever be perfectly tuned. The
interplay of string tension, string length, fret positions, nut
and saddle positions is always just as good as it can be. But the
compromise keeps getting better and better, and that's the road
to go.
While being a guitar teacher I realised the weakness of some
tuners concerning the low strings. Especially the low E-string is
a fierce demon that some machines just won't see. Also many cheap
tuners suffer from being chatty and displaying too much in too
little time. Values jump around and can't decide where to go.
[float Figure:
[Figure 1:
Tuning pipe
]
]
2 Evolution of digital tuners
Apart from the whistle, nowadays there are floor tuners, rack
tuners and clip-on tuners. These last could be a landmark in
useability, if they did their job at least acceptable. Depending
on the guitar, an experienced user is necessary - if the
vibration of the guitar is not high enough in amplitude the piezo
of the clip tuner does not get enough input. Still the difficulty
of low strings remained. With the advent of smartphones in almost
everybody's hands, a new age in digital tuning has arrived.
Suddenly a tuner is capable of a calm display and really neat
pitch detection. What's going on? How and why do they accomplish
this - whereas the former generations didn't?
3 Not necessarily from scratch
There are numerous tuner algorithms out there, for various
platforms. So there's definitely no need to write just another
tuner. But the thing with digital audio and signal processing is,
you need to start somehow from scratch to gain a basic
understanding of what is going on „under the hood“. We use Bjørn
Roches Code[footnote:
http://blog.bjornroche.com/2012/07/frequency-detection-using-fft-aka-pitch.html
] to start our way into the „black magic“ of the fft. It's a
tuner written in c and designed to be easy to understand and
follow. The outline of processing is as follows:
• Read audio for fft
• Low pass data
• Apply window function on data
• Apply fft
• Find peak in the data
• Determine frequency based on the peak value
Following this skeleton we will discuss the way of the audio
signal.
[float Figure:
[Figure 2:
ccgt 0.1 in the terminal
]
]
Execution
[/
applyfft( fft, data, datai, false );
/]
4 Digital[footnote:
“digitus” is a word for “finger”, so the term “digital” means
something like “countable with the fingers” - even if today's
machines have a bunch of fingers.
] audio signals
All sounds of the real world, that find their way into the
computer, live there only as numbers. While the vibrations of air
are continuously happening all the time, the computer just gets
one moment of a sound at a time. And every moment of the sound
the computer records is - a number.
[float Figure:
[Figure 3: Digital sampling
]
]
So if our program records sounds, these vibrations of air somehow
get translated into numbers. The numbers are defined by their
position in time, one position is one number which is called a
sample. The samplerate is how often per second the computer
listens. The unit for this is Hertz.
4.1 Perception of tones, sounds and noises
Let's imagine I produce a particular sound, for example tapping
the pencil against the table. The single sound of this would be a
kind of „tick“. When tapping faster, there would be a faster
sequence of ticks. Above a certain frequency, these ticks would „
collapse“ in our perception - into a tone with a frequency. From
20 Hz on upwards, we would perceive what could perhaps be called
a tone - at least there is a periodic vibration. I'm afraid I
can't do that with my pencil. In musical instruments there are
strings, membranes and lots of other things producing these
periodic vibrations. If you want to listen to the phenomenon of a
series of impulses getting faster and collapsing to a tone,
listen to the intro of „Gamma Ray“ by the krautrocker group
Birthcontrol[footnote:
https://www.youtube.com/watch?v=Vzlv7LFmLMg
]... a synthesizer does the job here.
The human ear is product of a long process of evolution, which
once was one of our life-saving abilities. This means, it can do
lots of really fine scanning and listening. But how is the
computer doing it? On which basis does he work? One prerequisite
- the samplerate - was already mentioned above. There is a fixed
rate of scans per second. If human beings can perceive a tone or
pitch from 20 Hz on upwards ... how often should the computer
sample?
4.2 How the computer listens
The Nyquist - Theorem explains, how often a signal has to be
sampled to be able to represent it digitally. Figure 4[footnote:
freundlicherweise von Peterpall - Eigenes Werk, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=8730560 zur
Verfügung gestellt.
] tries to show, how frequencies above the nyquist frequency
behave. In the middle you see a wave that gets sampled, that has
exactly the frequency of half the samplerate. That is an
important threshold for frequencies that get into the computer.
How come, that you can only represent frequencies up to half of
the samplerate?
[float Figure:
[Figure 4:
Nyquist theorem
]
]
Could the computer hear my pencil tapping at 20 Hz? Yes, if he
only listens frequently enough. Suppose I live directly next to a
street, on which exactly one car per minute passes past my house.
Supposed I listened hard all the time, car noises would become
louder at first. Then - when passing by - a climax is reached.
After that the noise fades away, until the next car is beginning
to come nearer. The level of noise is like a wave. But
unfortunately I sit in front of my computer with headphones on
and therefore cannot hear what is going on around me. Nothing
from the outside world gets to my ears. If I took off my
headphones every 60 seconds to check what's going on, I could get
the impression there are no cars on the street. That would happen
if I accidentally pick the moment when the outside noise is
lowest. It could as well happen I always listen exactly in the
moment when a car is directly in front of my house. Then I might
come to the conclusion, there's a whole lot of traffic. This
misconception of what is going on has its roots in the periodic
nature of my listening on the one hand, and the processes I
listen to on the other hand.
The computer is like me in the example: sampled audio signals are
not continuous, but a bunch of points with nothing between them.
Figure 5 shows this - the high frequencies are very fast “cars”
racing through the points while not really getting noticed. In a
periodic signal incredibly many “cars” are passing by with
different speeds; you can easily get false impressions about the
traffic. As human hearing can sense vibrations up to 20000 Hz, a
samplerate of 20000 would never be enough to represent such high
frequencies. Therefore you might have heard of the famous value
44100 Hz, roughly about the double of perceivable frequencies
plus a little headroom.
The signal inside the computer is not only discrete in time. Also
the strength of the vibration is represented as numbers. The
amplitude of the signal is - after being recorded by the computer
- not continuous any more. The louder a sample, the higher the
number on a given scale. This value represents a step of
loudness, and between the steps there are gaps again. Most audio
data has 8 or 16 bit samples, which means the number for one
sample can be that long or precise.
[/
recorder = new AudioRecord(AUDIO_SOURCE, SAMPLE_RATE, CHANNEL,
AUDIO_FORMAT, BUFFER_SIZE);
/]
If you see the processing of input data as some kind of “hearing”
, so this is a discrete process. The computer cannot be a unity
of perception and processing at the same time like the human
hearing. It's always going to be a finite buffer of numbers that
the computer processes. After finishing the processing, the
result can be viewed and the next buffer will be processed and so
on. Of course this happens very fast - so fast you could think
the process is continuous. The changes happen within
milliseconds. Nevertheless there has to be a defined region of
audio data, on which processing occurs and on which steps of
processing are defined. Here the buffer comes into focus.
4.3 Buffer: One at a time, please
Buffer size determines the amount of audio data on which
calculations are done. Suppose I work in the kitchen of a
restaurant and have the task of peeling potatos. As the potatos
are in the basement, I won't go there for every single potato,
peel it and put it into the water. Normal human beings would
fetch a bag of potatoes. This quantity gets peeled and eaten,
when that is done the next bag is fetched. It is unlikely that so
many potatoes get eaten for one lunch, but never mind the bad
example. The size of the bag is the buffersize for the
processing. The computer gets data all the time, but he can only
deal with it in defined portions. The audio system may need a
fine sample resolution in order to be able to represent the
desired frequencies - but buffer size also has its constraints
and should neither be too big nor too small. Maybe you have
already encountered the problem of latency while recording audio
and then tried to change your buffer size. While doing this you
often see powers of 2 as values.
[/
err = Pa_ReadStream( stream, data, FFT_SIZE );
/]
The easiest case is, your buffer size is equal to your fft size.
Of course there are more refined concepts. But for first we
divide the problem into reading audio data once - and then
analysing around on this “screenshot” of audio.
[float Figure:
[Figure 5:
Discrete values on the time axis
]
]
5 Fourier transformation
5.1 Change dimensions
Having read audio data into the buffer, the numbers are in memory
as time discrete values. They represent vibrations of air at a
given moment in time. Data is ordered on the time axis, and each
value has a certain “height” or amplitude. The imagination of a
waveform might help. The array of audio data is a vector of
numbers with the length of n.
The elements of this vector are float numbers with values from -1
to 1. The cector v is transformed to a vector c of equal length
by the fourier transformation:
After that, again we have a series of numbers of equal length.
But the numbers now represent frequencies, not amplitudes. After
the fourier transformation, the vector c
contains the frequency spectrum of the input signal. There was a
cocktail in your glass before, but now you have all the
ingredients separated in it - at least ideally. The cake was
ready, but now it is backwards engineered and divided into the
parts of its recipe again. The transformation takes amplitudes on
the time axis, and gives you amplitudes on the frequency axis.
See this happening in realtime on a waterfall display, and you
will know what I mean.[footnote:
https://phyphox.org/de/home-de/ software for physical experiments
] Spectral components are plotted as time advances. Dominant
frequencies are highlighted.
[float Figure:
[Figure 6:
Waterfall-display
]
]
But how does fourier transformation really work? What happens to
our input data, that makes it so meaningful afterwards? Let's say
we have a audio buffer of the length 8, and there are even some
values in it:
Fourier transformation would take this as input data:
It then computes the coefficients of sine- and cosine waves.
Fourier believed, that every periodic wave could mathematically
be described as the sum of sinewaves and their integer multiples.[footnote:
https://www.youtube.com/watch?v=7bi5_KC08Kg
] This process is called fourier synthesis. For a periodic
function f(t) with the period T, the definition of the fourier
synthesis is as follows:
The function is built up out of infinite partial functions, every
one depending on the number n. The base frequency of each partial
is t/T in the exponent of e, and n as its integer factor. Ideally
this should add up infinitely. The computer won't reach infinity
with his calculations, and we don't want to wait until he
eventually does. The frequencies of the input signal don't reach
until infinity as well, as it is built of samples and therefore
constrained to the nyquist frequency. So we modify the function
and reduce it to the length of the buffer. Additionally, we don't
want to have a synthesizer that builds soundwaves, but the exact
opposite: We want to determine frequencies in the input signal.
So we apply the inverse of fourier synthesis, the fourier
analysis. This is often called the discrete fourier transform,
because the signal we're working on is not continuous but
discrete. With this said, the vector c gets computed as follows:
Every sample in our input data is now represented by a element v
with the index j in the sum. This means: To get one single
element of the transformed vector c, a operation on the whole
audio buffer occurs. If you write the computation for one element
of c as row, and that for every single element of c, you get the
matrix version of the fourier transformation:
For every row of the fourier matrix, the index k stays constant
while j increases for every column. This gives a zero line as
first line, integer multiples of 1 in the second line, integer
multiples of 2 in the third line and so on. The first column is
zero, because the index j always starts with 0. The resulting
matrix is symmetric, a property that is used by the “fast fourier
transform” algorithm, in short fft. As a result of this highly
efficient algorithm, you find many code snippets just labeled “
fft”.
The more you get down into the matrix, the higher the exponent;
the higher the frequencies which are calculated. You'll see even
more reference to periodic functions, if we write the function
with sine and cosine waves using euler's identity.
You find the product of k and j in the above fourier matrix. This
is a sum containing a bunch of periodic waves, which are weighted
depending on how the input signal looks like. Depending on how
big v is at the index j, some frequencies get a greater value
than others. Fourier analysis finds out, which frequencies[footnote:
You might say circular motions.
] to add in order to get something like the input signal.
5.2 Frequency distribution in the result
How can I tell the user about the frequency of the tone that was
played, dependent on buffer and fft size? There is this output
vector which gives me some data the fft has computed. What do
these values mean? To find out, we should give our program some
parameters:
[/
#define FFT_SIZE (8192)
#define SAMPLERATE (8000)
/]
The signal gets sampled 8000 times per second, frequencies up to
approximately 4000 Hz can be detected. The audio buffer and
therefore the vector for the input data has a length of 8192, and
that's also the amount of complex numbers we have now as output
vector of the fft. The frequency f for a value from c is defined
as follows:
This apparently goes from zero to approximately 8000, so the
values of the frequency vector above 4000 are not meaningful.
Noteworthy here - every value in c is a central frequency. For
every k, the frequency value changes by
given our configuration. As a comparison check out these values:
A low samplerate combined with a large buffer minimizes the
frequency width, whereas a finer sampling combined with a small
buffer gives you a really coarse frequency grid.[footnote:
These frequency bands are often called „fft bins“.
]
In the code mentioned above, there are arrays of the length of
the fft for frequency, note name and pitch. While this is
implementation specific, it should still be mentioned that the
frequency array is initialised over its whole length with the
corresponding frequency value
The samplerate divided by size of the fft window gives you the
width of one frequency bin, its central frequency determined by
the index i.
[/
for ( int i = 0; i < FFT_SIZE; ++i ) {
freqTable[i] =
(SAMPLE_RATE * i) / (double)(FFT_SIZE);
}
/]
5.3 Pitches and names
The program needs an internal representation of how the
frequencies should be called as notes. Given a detected
fundamental frequency the code should know, which pitch
corresponds to the frequency. The twelve pitches can be mapped to
frequencies via an index. But of what frequencies are we
speaking? The a4 at 440 Hz is taken as a reference in most cases.
Knowing that one octave doubles frequency[footnote:
Exponential increase, by the way.
], I can identify 880 Hz, 220 or 110 Hz as notes with the pitch “
a”. But what happens in between? Maybe the guitar is really not
in tune, and the string is almost one semitone sharp or flat - or
the strings were completely changed out and have no “initial
value”.
As frequency exactly doubles while crossing one octave, the
frequency distance between semitones grows when ascending or
diminishes when descending. There is never the same numerical
distance between the frequency of semitones. That's why a
logarithmic scale is used to obtain a scale of equidistant
semitones. Now the question is, which frequency does the pitch
one semitone above a4 have? The octave - here the factor p which
means “twice of what was there before” - gets equally spaced in
twelve steps:
If I multiply any frequency by 2, out comes the octave. If I want
the tone to be just a little higher - for example the smallest
possible diatonic step of one semitone - the factor should be
less big. How much “less big” can be calculated, if one reference
frequency and the distance is known.
With this information, a program can generate note names from
frequencies - in what form or data structure you want to do it is
up to you as a decision of the implementation.
5.4 Peak
[/
// distance formula
v = this.ar[j]*this.ar[j] + this.ai[j]*this.ai[j];
/]
How does the program find the fundamental frequency? The given
code determines the energy at certain fft bins[footnote:
Auf stackoverflow „fft magnitude“
] by interpreting the result of the fft as a distance. In the
easiest case, an iteration over the array of results is done to
get the maximum value; that means we search the frequency band
with the greatest amplitude.
The low e-string of the guitar has a fundamental frequency of
82.5 Hz. But unfortunately, this frequency is lower in amplitude
than the first harmonic in the majority of cases. So what happens
with our simple pitch detection? It detects the highest
amplitude, which is in fact the harmonic and not the desired
fundamental that we hear. At this point, the fft is not good
enough for a reliable pitch detection. The fundamental which we
somehow hear but which does not get detected points to the fact,
that pitch perception is a psychoacoustic phenomenon. What we
actually hear does not seem to be determined solely by the
spectrum that a fft reveals. Whether fft is reliable, seems to
depend on its working area. If you want to get your guitar in
tune, and fft is the only tool - you will soon want to optimize
things. In order to do this it is necessary to dive deeper into
signal processing and pitch detection algorithms.
6 Filter
[float Figure:
[Figure 7:
Lowpass filter
]
]
In order to minimize the problem of missing fundamentals, a low
pass filter is used before doing the fft. Only frequencies up to
a specific threshold get into the buffer. Most high frequencies
would not be detected anyway. And in a tuning app, we do not need
high quality audio as we don't want to play it back after
processing it. Filtering out high frequencies and unmusical
noises reduces errors while processing the data. Additionally a
windowing function is used to smooth the audio screenshot at the
edges. These soft edges also reduce errors caused by rough
transitions, as the fourier transformation pretends the signal to
be periodic.
[float Figure:
[Figure 8:
Hanning - window
]
]
Synthesis
While not yet dealing with aspects of user interaction, the
implementation of a basic functionality was discussed. Simply
doing a fft gets you in the right direction, but unfortunately
only half the way. Nevertheless this is the first step towards a
solution of the realworld requirements. The stage is set, on
which further refinement can take place.
Tuning a guitar is a special case in the more general field of “
pitch detection”. Since electronic equipment became small enough,
it was employed in tuning. With smartphones in our hands, a
little revolution of computing power took place. Fourier analysis
can be done on most if not all of today's handheld devices. But
that's not state of the art anymore. There are numerous
algorithms for pitch detection out there. Fft is just one of the
tools applied in these algorithms, dealing with the frequency
domain of signals. If you feel you understand the numbercrunching
that is done in fourier transformation, go on and take a look at
the McLeod pitch method[footnote:
This could be sort of a follow up:
http://www.cs.otago.ac.nz/tartini/papers/Visualization_of_Musical_Pitch.pdf
] which has a really good paper online. The YIN[footnote:
A bit more difficult to read:
http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf
] algorithm is another more recent approach which is very well
documented online.
Have fun coding audio, and don't forget to play your guitar!
))
Here's the 
