But as a morse decoder pure and simple MRP40 is still the winner in my book. That is, after all, what it has been designed to do. This is perhaps understandable given that Skimmer is intended to be able to distinguish between multiple signals in a pile-up.ĬW Skimmer is the better program if you want to decode all the calls in a swathe of spectrum and if you want to link to your logging program so as to highlight new countries or prefixes and mark stations you’ve previously worked. CW Skimmer seemed more fussy and didn’t decode a signal unless you got it spot-on. It could track drifting stations and would adjust itself precisely to the signal if you didn’t click exactly on the trace. I also found MRP40’s AFC useful in locking on to signals. MRP40’s decoder is less laggy than CW Skimmer’s – text appeared sooner after it was sent. Skimmer also seemed on occasion to insert an spurious E at the beginning of some words or calls when I didn’t hear an extra dit. It decoded text more accurately and the spacing between words was better – CW Skimmer would often run words together then insert a space in the middle of a word. After listening to many QSOs I am still of the opinion that MRP40 is the best decoder. I ran both programs simultaneously decoding the same signal. A few days ago Paul PC4T commented to one of my posts that he thought CW Skimmer was better so I thought I would give it a try in case I was missing out. 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.For several years now I have been of the opinion that the best Morse decoder for Windows PCs is MRP40 by Norbert Pieper. 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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