I read in sci.electronics.design that Jim Thompson
4ax.com>) about 'Instantaneous (analogue) compression of speech
signals', on Fri, 7 Jan 2005:
John said:
[snip]
RF/IF clipping is not an option. The amplifiers concerned are analogue,
with transformer/rectifier power supplies. AS such they need minimal EMC
assessment and usually no testing. Introducing RF and/or digital
processing changes the situation greatly and involves significant extra
cost and development time.
I realize you ruled the method out as true RF/IF operation. There is the
baseband envelope clipping method which is absent RF/IF translation or digital.
I don't care what you do. Personally I think I kinda like the tanh method if
you can make it work like you want -- it is really simple.
Diff pair plus OpAmp, plus a DC loop to keep the diff pair balanced.
Do you mean feeding the op-amp d.c output back through a potential
divider to the base of the tail transistor?
I've now investigated the tanh(sin(x)) function in Mathcad. For 3 dB
reduction in peak voltage, and the transfer function for the long-tailed
pair normalized to y = tanh(x), the peak input voltage has to be 1.15
normalized volts, and this gives about 9% third harmonic and 1% 5th. An
interesting result is that the harmonic spectrum is nearly linear on a
decibel axis, the nth harmonic level being nearly -10(n-1) dB. n is
always odd, of course, for symmetrical limiting. This linearity applies
for any input signal amplitude that I have tried, but the scale factor
changes, of course.
3 dB doesn't sound much, but it halves the amplifier power required.
Now to find what it sounds like on real signals. I have a breadboard all
ready!
Incidentally, I obtained those results by using traditional 'analogue'
Fourier analysis. I tried to use the Mathcad 'fft' function, using 32
samples, carefully arranged so that the input waveform started and
finished at zero amplitude. It gave me results which I did not believe,
including a succession of even harmonics of significant but suspiciously
similar amplitude and a fundamental component much larger than 1. I
thought that maybe 32 samples was too few, so I tried 1024. The results
were even worse; the fundamental component amplitude exploded to about
17! I'm sure there is an explanation, but it concerns me that it would
be very easy to get a wrong answer, using 'fft', that wasn't so
obviously wrong as to ring alarm bells.