{"id":17871,"date":"2026-11-07T15:01:55","date_gmt":"2026-11-07T09:31:55","guid":{"rendered":"https:\/\/johnsonfrancis.org\/techworld\/?p=17871"},"modified":"2026-11-09T08:10:45","modified_gmt":"2026-11-09T02:40:45","slug":"can-i-decode-morse-code-transmission-during-cw-net-using-ai","status":"publish","type":"post","link":"https:\/\/johnsonfrancis.org\/techworld\/can-i-decode-morse-code-transmission-during-cw-net-using-ai\/","title":{"rendered":"Can I decode Morse Code transmission during CW Net using AI?"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">A few days back I had shown CW decoding with fldigi in my laptop. While many characters were accurately decode, many were not. Modern AI tools are significantly better at handling the real-world variables of a live net\u2014such as interference, changing speeds, and the unique &#8220;fists&#8221; of different operators\u2014than traditional algorithmic decoders.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"577\" src=\"https:\/\/johnsonfrancis.org\/techworld\/wp-content\/uploads\/2026\/11\/image.png\" alt=\"\" class=\"wp-image-18482\" srcset=\"https:\/\/johnsonfrancis.org\/techworld\/wp-content\/uploads\/2026\/11\/image.png 1024w, https:\/\/johnsonfrancis.org\/techworld\/wp-content\/uploads\/2026\/11\/image-300x169.png 300w, https:\/\/johnsonfrancis.org\/techworld\/wp-content\/uploads\/2026\/11\/image-767x432.png 767w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">How AI Handles CW Net Challenges<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Traditional decoders rely on rigid, absolute timing rules (e.g., expecting a dot to be exactly 50ms), which quickly break down when a human operator uses a straight key or when the net control station changes their sending speed. AI models approach decoding differently:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Neural Network Pattern Recognition:<\/strong> AI models are trained on thousands of hours of real-world CW audio, allowing them to recognize patterns even through heavy QRM (interference), QRN (static noise), and fading (QSB).<\/li>\n\n\n\n<li><strong>Adaptive Timing:<\/strong> Instead of strict millisecond cutoffs, AI analyzes the <em>relative<\/em> timing context to differentiate between a sloppy character and a pause between words, making it highly tolerant of varying rhythms.<\/li>\n\n\n\n<li><strong>Pre-Processing Integration:<\/strong> Advanced AI tools apply digital band-pass filters (e.g., 25Hz or 50Hz) before feeding the audio to the neural network, isolating the specific carrier frequency to pull weak signals out of the noise floor.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Ready-to-Use AI Software<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Several applications have integrated trained neural networks specifically for amateur radio use:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Morse Decoder AI (iOS\/macOS):<\/strong> This app uses dual neural networks\u2014one optimized for stable, machine-generated CW and another for the variable timing of straight keys. It decodes speeds from 10 to 40 WPM and includes built-in tone filtering to help decipher faint signals. This is a paid app available on the Apple App Store for iPhone, iPad, and macOS (limited to Macs with Apple M1 chips or newer).<\/li>\n\n\n\n<li><strong>Morse AI: Code CW Translator (Android):<\/strong> Features a &#8220;Neural Signal Purifier&#8221; designed to decode distorted, static-heavy, or merged Morse transmissions in real time, alongside a visual audio waveform oscilloscope. This is a freemium app available on the Google Play Store for Android. The base download is free, but unlocking its advanced neural network decoding features requires a paid &#8220;Pro&#8221; subscription.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">The Homebrew AI Approach<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If you prefer building your own station accessories, you can create a custom AI decoder. A single-board computer like a Raspberry Pi can be used to digitize the audio or capture timing data from a receiver, which is then fed via API to a Large Language Model. Because LLMs understand language structure, they process the sequence of short and long pulses contextually\u2014often accurately transcribing the message and guessing the correct word even if a few characters are completely lost to interference.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Building a DIY AI-powered decoder is a great way to combine amateur radio homebrewing with modern machine learning. Rather than feeding the LLM raw audio, the most effective architecture uses the Raspberry Pi to perform local digital signal processing (DSP) to extract the timing data, which is then sent to the LLM as text for contextual decoding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here is the standard unit timing that your DSP will try to capture before the AI steps in to correct human errors:<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">System Architecture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1.Interface the Transceiver:<\/strong>Hardware Setup.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Connect the audio output from your transceiver\u2014such as the headphone jack on your Easy BITX\u2014to your Raspberry Pi 5. Because the Pi 5 lacks a native analog audio input, you will need to use a basic USB sound card or an SDR interface to digitize the incoming audio.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.Capture Audio with Python:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Write a Python script using libraries like <code>pyaudio<\/code> or <code>sounddevice<\/code> to continuously sample the incoming audio stream. For a narrow CW signal, a low sample rate like 8000 Hz is more than enough and keeps the processing overhead low.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>3.Extract the Timings via DSP:<\/strong>Convert audio to data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Use a library like <code>numpy<\/code> to run a Fast Fourier Transform (FFT) on the audio chunks. Have the script isolate your specific sidetone frequency (e.g., 700 Hz) and record the exact timestamps when the tone starts and stops. This yields an array of durations for active tones (key down) and silent pauses (key up).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.Pass Data to the LLM API:<\/strong>Contextual decoding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Format the array of durations into a text prompt and send it to an LLM via its API (such as OpenAI or Anthropic). The LLM&#8217;s language comprehension allows it to accurately interpret &#8220;sloppy&#8221; timings that deviate from the perfect standard, guessing missing characters much like a human operator compensating for heavy static or fading on the 80m and 160m bands.<\/p>\n\n\n\n<p class=\"has-vivid-purple-color has-text-color has-link-color wp-elements-1 wp-block-paragraph\"><strong>My friends tell me that non AI based ones can be at most 70-80% accurate. What about the AI based ones?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Your friends are absolutely right when it comes to human-sent Morse code. Traditional DSP software like <strong>fldigi<\/strong> or <strong>CW Skimmer<\/strong> relies on hard-coded algorithms that expect perfect mathematical ratios (a dash is exactly 3x the length of a dot). If an operator is using a keyboard, fldigi is nearly 100% accurate. But once a human gets on a straight key with a sloppy &#8220;fist,&#8221; varying speeds, or there is QRM (interference), traditional software quickly drops to that 70\u201380% accuracy mark.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI-based decoders can push that accuracy to <strong>90% to 95% or higher<\/strong> under those same challenging, real-world conditions. Recent studies on neural network-based CW decoders regularly demonstrate success rates of around 94% on imprecise, human-generated timing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here is why AI handles real-world CW so much better than traditional software:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Training on Human &#8220;Sloppiness&#8221;:<\/strong> Instead of using a strict 3:1 mathematical rule, neural networks are trained on thousands of hours of actual human operators. The AI learns to adapt to an individual sender&#8217;s rhythm in real-time, easily deciphering characters even if the operator routinely runs their letters too close together or makes their dashes too short.<\/li>\n\n\n\n<li><strong>Spectrogram Processing:<\/strong> Rather than just listening to audio levels, many modern AI decoders use machine vision to &#8220;look&#8221; at the waterfall spectrogram. They read the visual patterns of dots and dashes out of the noise floor, which allows them to decode weak signals that are entirely buried in static to the human ear.<\/li>\n\n\n\n<li><strong>Context and Callsign Prediction:<\/strong> This is the AI&#8217;s biggest advantage. Traditional software decodes letter-by-letter in a vacuum. AI models are trained on standard QSO structures (CQ, RST, Q-codes) and databases of millions of amateur radio callsigns. If an AI decodes a garbled string it can use predictive modeling to figure out it was actually <code>...<\/code> based on the context of the net, something fldigi cannot do.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-vivid-red-color has-text-color has-link-color wp-elements-2 wp-block-paragraph\"><strong>These are the inputs I received online. Has anyone tried it out?<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A few days back I had shown CW decoding with fldigi in my laptop. While many characters were accurately decode, many were not. Modern AI tools are significantly better at handling the real-world variables of a live net\u2014such as interference, changing speeds, and the unique &#8220;fists&#8221; of different operators\u2014than traditional algorithmic decoders. How AI Handles [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":17872,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-17871","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-amateur-radio-ham-radio"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Can I decode Morse Code transmission during CW Net using AI? - Johnson&#039;s Techworld<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/johnsonfrancis.org\/techworld\/can-i-decode-morse-code-transmission-during-cw-net-using-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can I decode Morse Code transmission during CW Net using AI? - Johnson&#039;s Techworld\" \/>\n<meta property=\"og:description\" content=\"A few days back I had shown CW decoding with fldigi in my laptop. 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