TEST BUILD. This is an early, in-development model (test build), not a finished product. Everything below is a real experiment with honest mistakes shown as they happened. Results are a snapshot, not a benchmark. Watch the ~5:36 test. The interesting part starts around 4:18 on the right-hand track. 1. The test, in one line I pointed NeuroCW at a live web receiver and fed it weak, fading signals on 40m. The question was simple: when a signal nearly disappears into the noise, can the model still read the callsign a human ear struggles to hear? Figure 1. KiwiSDR in Chichester, UK, tuned to the 40m CW band. The audio was routed into NeuroCW through a PC loopback. 2. Setup Item Detail Receiver KiwiSDR, Chichester, UK (public web SDR) Band / mode 40m, CW mode selected Audio path Browser audio → PC loopback → NeuroCW Model Test build / early model (not a released version) Signal level App showed SNR fluctuating roughly from the mid-teens to high-20s dB, with d...
Call for data: real received CW recordings (WAV/FLAC) + a 100% verified transcript . Email kd8cec@gmail.com , subject "NeuroCW data" . Figure 1. Everything NeuroCW learned came from the bottom panel: a simulated channel. Now it needs real signals. 1. Why real data In Part 1 , NeuroCW scored 5.07% CER on synthetic tests, but its accuracy on real on-air signals hasn't been measured. Just two real recordings were enough to expose a blind spot: Figure 2. On a synthetic heavy-weight keying test, one targeted fine-tune cut the error from 13.4% to 0.1%. Verified real recordings would turn findings like this into a benchmark. 2. What we need Figure 3. Checklist for one submission. 3. How to contribute Figure 4. Four steps. Several recordings per email is fine, and share links work for large files. Transcript rules: write exactly what was sent, including the sender's mistakes. Put prosigns in angle brackets (<AR> <SK>). For Korean or Japanese,...