Dec 182017
This program will decode morse code coming from your short-wave receiver using a Sound Blaster and real-time FFT routines.
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This program will decode morse code coming from your short-wave receiver using a Sound Blaster and real-time FFT routines.
File Name File Size Zip Size Zip Type
FFTMORSE.C 24505 6256 deflated
FFTMORSE.DOC 19677 7508 deflated
FFTMORSE.EXE 11755 11265 deflated

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Contents of the FFTMORSE.DOC file

/* Warning! This documentation contains extended characters! */

Program: FFTMORSE.ZIP (freeware)
Date: 1 May 1992
Version: 1.0

Turbo-C flags: -mt -1 -d -f- -A -K -G -O -w

Copyright (c) 1992 Franois Jalbert ([email protected])

A few more copyright notices so everybody is happy:

Sound-Blaster (c) 1989-1991 Creative Labs Inc
Turbo-C 2.0 (c) 1988 Borland International
Sound-Blaster C Library BLAST13 (c) 1991 Joel Lucsy
LZEXE 0.91 (c) 1989 Fabrice Bellard


This program will decode morse code coming from your short-wave receiver.
Extensive attention as been paid to efficiency and to robustness. With a 286/12
and a Sound-Blaster 2.0, it operates at about 10kHz and gives me 100% accuracy
on good quality signals and good quality (!) key operators. It does not require
any special hardware or short-wave receiver capabilities; a simple dynamic
microphone hooked to your Sound-Blaster will do the job.


Since I was given an old 1950 tube short-wave receiver about a year ago to heat
up my room in winter, I had been dreaming about being able to understand the
morse code I could pick up around the 40 and 80 meter bands. I quickly learned
the morse code, but I found that decoding was not feasible in practice. First,
I lacked speed. Second, I had to concentrate most of the time on my short-wave
receiver since the signals keep drifting back and forth, therefore requiring
constant fine tuning adjustments from my part. There is simply no time left to
decode any morse code!

My dream finally became possible when I bought a Sound-Blaster 2.0 last month.
Using its Digital Signal Processing (DSP) unit, it is possible with a micro-
phone (or a direct line) to digitize an analog signal at a rate of up to 15KHz.
That is more than enough for the task at hand.

Unfortunately, the Sound-Blaster comes with no information on how to interface
with it in assembly or in some higher-level language. I gave SIMTEL a shot and
got lucky; I located the Sound-Blaster C library BLAST13 of Mr. Joel Lucsy. It
took only a few minutes to disassemble its DSP routines and get a basic under-
standing of what's going on. Direct operation is indeed trivial.

Nevertheless, I decided not to use assembly for this little project since this
is invariably time consuming. Instead, I used C and the library I had found. If
worse came to worse, I might stick in a couple of assembly functions, but this
turned out not to be necessary. I must express my gratitude to Mr. Lucsy for
opening the world of the Sound-Blaster to all of us.

Another important problem is the low quality of signal one must typically face.
There is an omnipresent strong background noise, the morse signal comes and
goes, its frequency changes all the time, and you usually get two or three
different morse signals (of different tone frequency) for a given tuning setup.
Basic experimentation confirmed what I had suspected all along. It would be
necessary to perform a Fourier analysis in order to filter out noise and
undesirable signals. It better be a fast one too!

If all goes well, I will end up with a series of tone quantified in terms of
duration. It should be relatively easy to program a simple learning algorithm
which would quickly get accustomed to a given key operator. Automatic decoding
would be snap from that point on. This last step should be very dependent on
the operator and his regularity. I bet some pretty fancy keys are in use today.
They probably insure a good uniformity of the morse code sent. If that's indeed
the case, interpreting should not be very difficult.

There is no program performing the function of FFTMORSE available at SIMTEL as
of now. The closest thing I could find involved building some electronic
interface between the short-wave and the serial port of the computer. Close but
not good enough for me. I wanted something simpler using current technology.
Therefore, I believe that this project was justified and I am happy to hereby
contribute to the public domain.

This program has been tested on VGA only. It should run on mono. It has been
tested with a Sound-Blaster 2.0 only. It should work with the 1.0 and 1.5
versions. I also assume that the Sound-Blaster Pro is hardware backward
compatible. Finally, it has been tested on a 12MHz 286 only. It should run on
faster machines as well and it does use 286 (186?) or above instructions. In
fact, the Sound-Blaster card itself is likely to be the cause of any
difficulties you may experience with a 486/33 or a 486/50!

Feel free to pass along and share copies of my work. If you have any comments
or suggestions, I would be very happy to hear from you. My internet e-mail
address can be found at the top of this brief document.

Functional Description:

The main functions of this program are:

Initialization function begin();
Fast Fourier transform function FFT();
Tone detection function FFT2tone();
Learning function learn();
Morse interpreter function tone2morse();
Text interpreter function morse2text();
Keyboard handling function handlekey();
The usual main function main();

and I would like to briefly introduce them so you will have a better under-
standing of what my program does, does not, and what the various interactive
functions available do.

The initialization function clears the screen, initializes the Sound-Blaster
DSP, detects whether a mono or a color card is present by the video mode
currently in use (7=mono), and draws the various screen elements.

The fast Fourier transform function is quite complex and warrants a section on
its own which you will find below. All I want to say at this stage is that 32
values are fed from the Sound-Blaster as fast as it can deliver them. The FFT
computation then takes place and this results in 15 short integers in the range
[0,32] representing the activity in 15 frequency ranges.

It is also possible to read 64 values from the Sound-Blaster and to discard
every other one. This is called a mixture ratio of 1:2. You can also read 96
values and discard 2 of them out of 3; a mixture ratio of 1:3. And so forth.
A non trivial mixture ratio is sometimes desirable for two reasons.

First of all, as we saw above, information is read from the Sound-Blaster in
short bursts, with a pause in between to perform the FFT calculations. This
irregularity somehow introduces noise in the Sound-Blaster output. A mixture
ratio of 3 or 4 totally eliminates this noise since data read is more regularly
spaced in time.

Second, varying the mixture ratio allows you to control where one particular
tone will show up in the frequency display. The usual gaussian curve moves up
and down as the mixture is changed. This also makes it possible for you to
better isolate some morse tone buried under other ones of different frequency.

The tone detection function turned out fairly straightforward. I wanted
something which would detect the presence of a tone, presence usually taking
the form of a gaussian curve in the frequencies displayed. I decided out of
simplicity not to try to be clever and to automatically detect the presence of
such a dome. This seemed too problematic when one considers the level of noise
often present and the multiple domes also occasionally encountered.

Instead, I let the user select which frequency area he considers to be the
relevant one. Such a relevant frequency area is made up of 5 consecutive
frequencies, and this area will determine the presence or absence of a morse
tone. This can, of course, be adjusted at all time by the user.

Activity detection is performed by first computing the average activity of all
15 frequencies extracted. This is used to get an idea of the uniform noise
level. The total activity in the relevant frequency area is then examined and
if this activity is at least 5 above what the average would call for, the
relevant frequency area is considered active at that time. This value of 5 is
arbitrary, but seems to work well in practice. However, it is recommended to
adjust the audio level of your short-wave receiver as low as possible at all
time, in order to keep the confusing background noise to a minimum.

I also discovered that activity in the relevant frequency area may vary
somewhat, even though a continuous tone is present. This seems to stem from the
nature of the Fourier transform. Therefore, I added a short term memory to my
tone detection algorithm. A tone is considered present is at least one of the
last 4 cycles turned out active as defined above. Once more, this seems to work
very well in practice.

The learning function collects a total of 8 dots and 8 dashes, and computes an
average duration for each of them. This is used to decode tones into morse
basic elements. To differentiate between a dot and a tone initially, this
function first waits until it encounters a dot-dash or dash-dot pair. This is
easily detected by comparing the duration of each consecutive pair. Once such a
significative pair is detected, the statistics can be compiled.

What happens if you switch station and the new key operator is somewhat faster
or slower than the previous one? I decided once again not to try to be smart
and to detect that occurrence automatically. If you think about it, you will
find that this can get quite tricky in the presence of noise and a few ghost
tones. Instead, you have to manually indicate to the program the need to
recompile new key operator statistics.

The morse interpreter function receives a series of tone presence/absence and
their duration. It is a simple thing to compare these durations with the
statistics compiled for the current key operator, and to deduce dots and
dashes. The statistics are also updated since the current key operator may be
warming up and may be slowly picking up speed!

The text interpreter function receives a series of dots, dashes, and spaces.
These are easily translated into their equivalent ASCII values and displayed in
a window on the left. I used the international morse code as I found it in my
dictionary. I was surprised to see that it includes a few accentuated letters.
So be it, their support won't do any harm.

The keyboard handling function is called only if a key pressed has been
detected. The appropriate parameters are adjusted and there's really nothing to
this function otherwise.

The main function is made of two loops embodied one into the other. The most
inner one performs KCHECK cycles each involving 32 sampled values before
allowing the keyboard to be checked for a key pressed. The outer loop operates
SCHECK times before allowing the update of some secondary information (key
operator statistics and actual sampling rate) on the screen. These loops insure
that most of the CPU time is spent on the main task.

So here you have it, a complete informal description of what the program does
and how it does it. I do admit that this remains a little bit crude, but I just
had one week to spend on this. Although I do not plan on releasing future
versions, I'll be happy to hear from you. So please do not hesitate to write!

FFT Technical Notes:

The most complex element of this small project is by far the fast Fourier
transform algorithm I had to develop. My first prototype was a simple direct
application of discrete Fourier transforms. I used the 287 for the floating
point operations, but the resulting code turned out too slow on my 286/12. A
long integer version also proved too slow. Before jumping into assembly, I
decided to give a short integer version a try. It turned out not too bad. Here
is first of all the background information concerning it.

The Fourier polynomial on an interval [0,2] divided into 2N segments reads:

a0 N-1 aN
p(x) = + a cos(lx) + b sin(lx) + cos(Nx)
2 l=1 l l 2

I use 2N intervals since this even number results into more symmetry for the
trigonometric values involved. The underlying knots are:

x = k for k=0,...,2N-1
k N

The usual analysis will yield the following discrete Fourier coefficients:

1 2N-1
a = f(x ) cos kl for l=0,...,N
l N k=0 k N

1 2N-1
b = f(x ) sin kl for l=1,...,N-1
l N k=0 k N

I will immediately discard a and a and these shall not be computed.
0 N

It is obvious that some look-up tables can be computed in advance to speed up
things later on. We therefore have the following definitions:

f = f(x ) for k=0,...,2N-1
k N k

c = cos k = cos(x ) for k=0,...,2N-1
k N k

s = sin k = sin(x ) for k=0,...,2N-1
k N k

The quantities we want to compute can now be restated as:

a = f c for l=1,...,N
l k=0 k (kl)%(2N)

b = f s for l=1,...,N
l k=0 k (kl)%(2N)

where % stands for the usual modulo operator.

This is exactly what the first version of my short integer Fourier transform
did. In order to avoid short integer overflows and to get the maximum number
of bits of precision in the final result, I did make a few scaling changes:

1) The values coming from the Sound-Blaster lie in the interval [0,255] and I
rescaled them into [-128,127] in order to better repartition the bits. This
helped tremendously.

2) I didn't include 1/N in the f 's involved.

3) I multiplied the s 's and the c 's by 16 to get 4 bits of integer precision.
k k

4) The resulting a 's and b 's are divided by 2048 to bring them into [-16,16].
l l

5) The final results are a + b which all belong to the interval [0,32].
l l

This computation of Fourier coefficients implies only short integer operations,
half of which are unfortunately multiplications. This first attempt worked
reasonably fast, but not fast enough. I wanted to have a very good sampling
rate before getting into the next steps. This will be a long chain and if the
starting information is scarce and/or of poor quality, I fear that this may
hurt me later on.

I tried to cheat a little bit and to combine the Fourier coefficients as in:

d = f c + s for l=1,...,N
l k=0 k (kl)%(2N) (kl)%(2N)

This would cut the operation count by two, but I found the results not so good.
This is equivalent to using a different mathematical basis, which is not
orthogonal anymore, and which probably has a different physical interpretation
than the usual frequency response interpretation I wanted. So I reluctantly
went back to the previous separate Fourier coefficients approach.

I looked into the classical Fast Fourier Transform algorithm, but it offered
only an O(N log N) complexity versus the current O(N*N) count. Not good enough
to warrant the trouble, I thought. Plus it would involve a lot of playing with
array indices, arrays I would rather eliminate altogether...

So I expanded the equations for the Fourier coefficients and spent two days (!)
rearranging terms, finding common sub-expressions, and gradually incorporating
that into my code. There was a lot of repetition present, and it obeyed the
intuitively expected regularity rules. I eventually came up with a tight and
super fast 32 knots version. It is one to two orders of magnitude faster than
my first attempt! Not a single array is involved in the middle computations,
but a lot of short integer temporary variables are required. I am sampling not
to far from the maximum rate supported by my Sound-Blaster. No doubt any 386
and 486 will be working at maximum sampling rate with still plenty of time to

The courageous ones will take a look at function FFT() in my program. Please
feel free to double check everything and to report to me ASAP!!!


There are no command line parameters, just type FFTMORSE and there you are.
However, a number of keys are in use from within the program.

This toggles FFT on and off. Morse decoding requires the FFT option to
be active. This switch was added to make it possible for you to get a
very good sampling rate while tuning your short-wave receiver. A high
sampling rate improves the quality of the sound coming out ot the
Sound-Blaster. Once you have an interesting station, you should toggle
FFT back on to start decoding the incoming morse code. On fast
computers, this key is probably not very useful.

<+> These change the mixture ratio by throwing away samples every now and then.
<-> This makes it possible to move the signal up and down the FFT display until
it is located more or less in the middle. This is where I suspect the
Fourier analysis is at its best. These are only operational if the FFT is
enabled. Too high a value (say 10) is not desirable since this throws away
too much information and slows down everything since the Sound-Blaster can
only supply 15Kb of information per second at best.

<*> Used to toggle learn mode where the program will accumulate statistics
about typical dot and dash lengths. After a while, morse decoding will
resume on its own. Use this whenever you switch station or after
encountering a lot of noise which may have confused the program. Indeed,
the program continuously slowly adapts to changes of rhythm in the morse
code received.

Used to move the frequency window of interest up and down. This is
very useful in case the current tone frequency is not exactly in
the middle of the FFT display, or if several morse signals of
different tone frequencies are active at the same time. In the
latter case, you can isolate the signal of interest for you.

Exit the program.

Note: In order to avoid spending too much time asking the BIOS if a key has
been pressed, key pressed checks are performed only every once in a
while. On my 286/12, that's about once every two seconds. So do not
expect immediate crisp response following your keyboard entries. Response
time also increases with the mixture ratio (keys <+> and <-> above).

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