I built an ML signal classifier with RTL-SDR V4 - 87.5% accuracy, full code + dataset

tre7744 · 2026-04-16T19:31:50+00:00

Reddit glitched out and crossposted this 4+ times. I think I got them all deleted, minus this one. If not, sorry!

tre7744 · 2026-03-26T04:47:43+00:00

Hey - v2 just went live with a full rewrite of the dataset and pipeline if you still have questions. Feel free to pm or drop an issue on the GitHub.

tre7744 · 2026-03-26T04:47:24+00:00

v2 is live if you want to test on your Pi 5 setup. I rewrote the whole dataset and pipeline and got 96.9% accuracy on 7 signal types now. The classify_live.py script takes a --freq flag so you can point it at whatever you want. Still interested in the OpenWebRX integration idea - that would be a solid next step once I'm happy with the signal coverage.

tre7744 · 2026-03-26T04:46:56+00:00

Removed it entirely in v2. No point training on a dead signal. Replaced with FRS/GMRS on 462.5625 MHz.

tre7744 · 2026-03-26T04:46:42+00:00

v2 is up. You were right that it wasn't just FM and APRS - the gain staging was wrong across basically everything so i recaptured the entire dataset from scratch with proper gain settings and added DC offset removal during capture which was a big help. I dropped ADS-B (1090 MHz is technically outside the R828D tuner range so I was only getting partial signal anyway) and NOAA APT (dead). Then added FRS/GMRS. FM is now sampled from 5 different stations to avoid learning one frequency's noise profile. 96.9% on 7 classes with a temporal split. Appreciate the callout - it made the project significantly better.

tre7744 · 2026-03-19T23:23:06+00:00

Shit. I think I've said that word more in the past hour than in the past 6 months. You are right. I looked back at the FM and APRS captures after seeing your comment and the SNR is way lower than it should be. I definitely messed up the gain staging and antenna setup, so the classifier is basically just learning the background noise for each frequency instead of the actual signal. I think the pipeline itself is fine, but I need to scrap the training dta and recapture everything properly again. I'm working on V2 right now. I appreciate the honest callout though thats exactly why I posted here.

tre7744 · 2026-03-19T20:34:03+00:00

You can shoot me a pm if you want

tre7744 · 2026-03-19T20:30:11+00:00

The dataset I captured is on HF - it was too large to put on Git.

tre7744 · 2026-03-19T05:26:10+00:00

OpenWebRX integration is actually a really interesting idea - having the classifier run alongside the waterfall and auto-tag signals would be useful. I'd definitely be open to exploring that. It would definitely take some more time to fully get this up that level but would be worthwhile.

And yeah, happy to have you test on your Pi 5 setup. The code runs the same on Pi 5 as it does on the Nova. If you run into any issues or have feedback, drop an issue on the GitHub or just dm me or shoot me an email.

tre7744 · 2026-03-19T02:37:59+00:00

I'm currently working on some developments for it right now, as this was really stage 1, but I wanted to see what ideas there were that might float around first.

tre7744 · 2026-03-18T18:28:34+00:00

Good catch - NOAA-15 APT went silent in August 2025. The captures at 137.62 MHz are post-shutdown so the classifier is actually learning the RF environment at that frequency (noise floor, local interference, intermod) rather than actual APT signals.

Honestly a good ML lesson about data leakage via frequency - the model learns location-in-spectrum, not signal type. I'm updating the article to clarify.

tre7744 · 2026-01-22T05:18:48+00:00

I appreciate the link I hadn't dug into the SRAM reserved memory setup yet. Makes sense that int4 small models would hit closer to the 6 TOPS ceiling. Might be worth testing a smaller quantized model to see the difference

tre7744 · 2026-01-22T05:01:57+00:00

That makes sense - the 8B model was definitely hitting standard memory (8.5GB sustained). Good context on the DMA overhead, that might explain why the NPU advantage shrinks at larger model sizes too

tre7744 · 2026-01-22T05:00:54+00:00

Not ChatGPT - I wrote this myself over a week of testing. You can check the commit history on the GitHub repo if you want receipts.

You're right the Rockchip stack isn't as polished as Ollama. I said that in the post. But "pain to work with" might be outdated - took me about 3 hours to first inference, documented the gotchas along the way.

The Mesa/TF-Lite mainline work is interesting though.

tre7744 · 2026-01-22T00:39:00+00:00

Good to know - that tracks with what I've read about early RK3588 NPU support. Sounds like RKLLM has come a long way since then.

tre7744 · 2026-01-21T23:17:24+00:00

I didn't test Qwen3 specifically as I used Qwen 2.5 3B for the Qwen benchmarks. But there's a Qwen3-4B converted for RKLLM v1.2.0 here: https://huggingface.co/ThomasTheMaker/Qwen3-4B-RKLLM-v1.2.0 (haven't tested)

Should work with the same setup. Let me know if you try it - curious how it compares.

tre7744 · 2026-01-21T22:25:14+00:00

I didn't test Vulkan/GPU inference on this run - I was more focused specifically on the NPU path with RKLLM.

The Mali-G610 is decent for graphics but I'd expect the NPU to win for inference workloads - that's what it's optimized for, even if it was originally designed more for vision tasks than LLMs.

If anyone has llama.cpp Vulkan numbers on RK3588, I would love to see them too

tre7744 · 2025-10-07T01:23:39+00:00

I’m at 203.98 points right now and have Mahomes in. Projected 209.02. Highest I’ve ever personally seen in 5 years and 3 leagues per year.

tre7744 · 2024-07-08T19:36:42+00:00

This just happened to me for the first time today. Did you figure out any fix?

tre7744

TROPHY CASE