![]() I took a different approach and started using "Deep Learning" AI and wrote a decoder that can learn Morse code from labeled audio files. ![]() Instead of adding more complex algorithms to track the speed, estimate noise level, tune the filter bandwidth, find the peak signal frequency etc. Over the last two weekends during the deep freeze in New England I started a new project to figure out a different way of building a Morse decoder. I have written software for multiple different Morse decoders over the years to help me to follow those high speed conversations, but these decoders all seem to have flaws - with the real world noise, interference and signal fluctuations they tend to decode garbage. CW is a tough mode to master but I have seen hams that can listen and understand high speed CW conversations effortlessly with a speed that I could never achieve. I learned CW when I started my ham radio hobby, and spend many hours practicing in the Signal Corps in the Finnish Military. This mode is a simple digital communication that humans can learn by practicing hundreds or hours. One of the popular modes of communication in ham circles is known as CW (continuous waves) also known as Morse Code. When another ham responds to your call from some 17,000 kilometers away to make a contact it surely feels like magic. While we have Internet and instant messaging it is somehow very satisfying to setup a small portable radio station on a remote island in Pacific and using less power than a flashlight to bounce radio signals from ionosphere to reach out to the other side of our planet. Sometimes I take the radios with me to some more exotic location and attempt to make contacts with fellow hams across the world. I have been a ham (amateur radio operator) for over 40 years and love to listen the HF radio bands. I have been interested in Machine Learning for several years and trying to figure out how to build a better Morse decoder using ML techniques. Well, for some of us this gives an opportunity to delve into the mysteries of Deep Learning and Artificial Intelligence. Hovering over a character in the message shows the dits and dahs for that character.What can you do when a Polar Vortex lands over North America and it is just too cold to go outside? The message is decoded and typed out in the page as you send it, with unrecognisable characters shown with a "#". Which one sends a dit (left or right handed) can be changed. This demo is configured to use the "Z" key for one paddle and the "X" key for the other. With the toggle switch you can change to "iambic B" mode which adds one extra dit or dah to the end of the sequence. The keyer defaults to "iambic A" mode which sends alternating dits and dahs while both keys are pressed. ![]() Pushing the paddle one way sends a string of repeating dits and pushing the paddle the other way sends a string of dahs.Īn "iambic" keyer uses two paddles side by side and therefore lets you press both at once ("squeezing" them): in this mode dits and dahs will alternate. ![]() Instead of having to precisely time the dits, dahs and the spaces in between a paddle/keyer combination does a lot of the timing for you, using a speed setting of your choice. A keyer (combined with a "paddle") makes sending Morse code easier than the traditional "straight key" which most people would imagine is used.
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