Why the TouCans CW AI Isn't Decoding Real CW Yet

Yesterday I posted about ChatGPT5 writing a TensorFlow CW decoder. It's actually decoding clean CW from its training data, but when I feed it audio recorded from an actual HF receiver, nope.

I asked ChatGPT to write me a script to inspect the recorded audio and found out why this morning. The first thing I should point out is that this particular model is learning Morse code the wrong way. It is defineitly comparing dots and dashes. It's based on a vision neural net and so... The second thing I should point out is that it's learning Morse code faster than I did, so, you know, that's pretty impressive.

OK. Here's why the AI isn't understanding RF-borne CW yet. Here's a spectrogram that corresponds to its self created training data. This particular one is from the video I posted about yesterday. The video is included again below so you can listen to the code as well.

This is fairly impressive in that you can in fact actually read out the dits and dahs. Now, here's fairly clean CW from the Utah SDR.

There are a few different issues here that are immediately apparent. First, there is much more noise on the signal. Second, the RF audio signal is at a higher frequency than the training data.

There are two immediate paths forward I'm going to pursue:

1. Create training data that includes much more noise

2. Create training data at a wider variety of frequencies.

Decode video with training data audio corresponding to the first spectrogram above.

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