![]() ![]() So perhaps I am overly optimistic on what kind of labeled training material can be made available and how well these ML models can learn the signals from noise and interference. However, when highway gets filled with snow and visibility is down to 10 yards in a blizzard typically these auto pilot systems will disengage. Self driving cars like Tesla can keep the vehicle on the lane while driving on a highway during sunny summer conditions. Note that I have not proven this premise yet, so this view is just based on general deep learning principles. Arduino Morse Decoder 1 When all this interest in radio started a few years ago I remember putting something similar to amateur radio project kit into Google in the hope of finding something to build which would teach me a few things. I think the premise of deep learning approach is to provide enough labeled training material so that the decoder can learn to handle different RF band conditions automatically, without user intervention. ![]() ![]() The format in which morse code is sent: Characters are encoded by substituting them with combinations of. Although my approach works, it feels like a very amateurish way of doing things in python. Also, they tend to perform poorly when signal-to-noise ratio drops down to -6. It handles all characters available in morsecode. That is why you see dozens of buttons to adjust manually the frequency, detection threshold, speed, filter bandwith, autogain and many other parameters based on the RF band conditions. 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.Existing decoders require much less resources for sure, but are also susceptible to noise, interference and poor signal-to-noise ratio. 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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