The unit automatically adjusts itself to the speed of the incoming Morse code and displays the speed on the LCD.īudd’s original open source project doesn’t actually use an Arduino module as such, instead he incorporates the Arduino’s processor chip and clock components directly onto a ready-made decoder PCB. All functions are controlled by three push buttons along the top of the unit.
#WB7HFC MORSE DECODER SOFTWARE#
It includes a software controlled noise impulse filter, an automatic input-frequency tuning circuit and a switchable Farnsworth setting for receiving Morse that uses Farnsworth timing. The decoder will receive Morse code from an audio source, decode it and display it on a built-in 4 line, 20 character LCD. The VK2IDL Morse Decoder is based on an excellent open source design by Budd Churchward, WB7FHC. And so, the idea of building my own Morse decoder was born. In the process I was curious to note that there were a number of hardware and software Morse decoder units available, although reports on their performance varied. I started looking for on-line Morse training websites in the hope of recovering some of my proficiency. I had studied Morse Code back in the 1980’s as a requirement for my advanced AOCP and used it regularly enough to maintain a reasonable proficiency for a number of years. The idea of building a Morse decoder is the result of having recently returned to amateur radio after a long absence. Conventional wisdom holds that the best way to learn a new language is immersion: just throw someone into a situation where they have no choice, and they’ll learn by context.VK2IDL Morse Decoder displaying live Morse from the VK2WI automated Morse practice transmission on 3.699 MHz. Militaries use immersion language instruction, as do diplomats and journalists, and apparently computers can now use it to teach themselves Morse code.
The blog entry by the delightfully callsigned reads like a scientific paper, with good reason: really seems to know a thing or two about machine learning. His method uses curated training data to build a model, namely Morse snippets and their translations, as is the usual approach with such systems. But things take an unexpected turn right from the start, as uses a Tensorflow handwriting recognition implementation to train his model.
Using a few lines of Python, he converts short, known snippets of Morse to a grayscale image that looks a little like a barcode, with the light areas being the dits and dahs and the dark bars being silence.
The first training run only resulted in about 36% accuracy, but a subsequent run with shorter snippets ended up being 99.5% accurate. The model was also able to pull Morse out of a signal with -6 dB signal-to-noise ratio, even though it had been trained with a much cleaner signal. Other Morse decoders use lookup tables to convert sound to text, but it’s important to note that this one doesn’t. By comparing patterns to labels in the training data, it inferred what the characters mean, and essentially taught itself Morse code in about an hour. Posted in Machine Learning Tagged cnn, CTC, cw, lstm, machine learning, morse, SNR, tensorflow Post navigation We find that fascinating, and wonder what other applications this would be good for. What people forget is that adults do not get exposed to the same basic level of interactions that kids do. People are also less helpful or patient when asking for unknown words or explanations. The amount of necessary data and correlations is just not there, the information is way too “high-level” and specific to learn just by “sink-or-swim”.Īn adult learns much better by “compressed learning” or difference learning.
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