BEbeaugunderson·Oct 13, 2023
code for a CNN-based approach here:
https://github.com/randaller/cnn-rtlsdr
In Signal Identification Wikirandaller
cnn-rtlsdr
Python2018–2023
randaller / cnn-rtlsdr
cnn-rtlsdr
Deep learning signal classification using rtl-sdr dongle
3Distinct people
3Mentions
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339GitHub stars
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Deep learning signal classification using rtl-sdr dongle
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
3 sourcesML feels like an overkill for a single signal. If you want to process large, wideband scans, you could iterate over peaks and check things like: does phase shift create any interesting set of points, does PLL find consistent amplitude keying, does the frequency move around the centre. That covers 99% of what you're going to find in the wild. The first part (transform the signal) is definitely not great for ML, but the second part (does the result look like set of points / digital on/off key / voice) could be classified that way.
There are some existing projects like https://github.com/randaller/cnn-rtlsdr (that one tries to identify more specific tv signals though)
In Signal Identification WikiDeep learning signal classification using rtl-sdr dongle
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