Svar till: Audio decoding

[rich.g.williams]

“The integrating decoder currently works like this: It looks for dashes by integrating the signal multiplied by a dash-like folding function over a time interval that is slightly longer than one dash, thereby sensing the edges as well as the bulk of the dash. This procedure is repeated at a great number of points along the entire telegram, of the order every 10 ms”

That’s very interesting the “dash-like folding function” sounds like Fourier or convolution/deconvolution mathematics.

For the sake of interesting discussion there are many algorithms in pattern recognition etc that look for patterns (or images) regardless of exact dimensions. It could be possible using your approach to look for letters rather than individual dots/dashes. It could even be possible to look for words then you are soon getting into the realms of predictive text and using AI chips or software to do the job.

“However, my impression is that the ear and brain is unbeatable when the signal to noise ratio is poor” That’s interesting, That got me thinking about why? In part there is a lot of redundant information in a Morse code stream, people know what’s coming next and what makes sense. Ideally to test your software (or any future versions) a stream of random morse would be useful and then compare efficiency of your algorithm versus human Morse reader.

So one practical and probably useful idea that occurred is as follows:- Consider receiving the SAQ carrier window at 17200 Hz and also one or two nearby windows say windows at 17000 Hz and a window at 17400 Hz with say a 100Hz bandwidth. Now noise is broadband so you can expect the noise pulses to appear in all these three windows. Whereas the SAQ carrier will not appear in the other two windows. This gives a way to weight the validity of the SAQ Morse stream and then either to subtract the noise pulse, or to define an extra condition (Carrier, No carrier or Noise), or the noise information could be an input to your “folding function over a time interval” approach.

Well done interesting work!