TorchSig: A GNU Radio Block and New Spectrogram Tools for Augmenting ML Training
Abstract
TorchSig uses machine learning (ML) to detect
and classify digitized radio frequency (RF)
signals. Recent updates and improvements to
TorchSig are given, as well as novel features
for image-only spectrogram generation and training,
reducing memory and computational burdens
and making training much faster. A new
GNU Radio out-of-tree (OOT) block is provided
which uses a TorchSig ML model for detecting
signals in real-time.
Published
2024-09-24
How to Cite
VALLANCE, Phil et al.
TorchSig: A GNU Radio Block and New Spectrogram Tools for Augmenting ML Training.
Proceedings of the GNU Radio Conference, [S.l.], v. 9, n. 1, sep. 2024.
Available at: <https://pubs.gnuradio.org/index.php/grcon/article/view/147>. Date accessed: 05 nov. 2024.
Section
Articles
- I grant gnuradio.org a perpetual, non-exclusive license to distribute this article.
- I certify that I have the right to grant this license.
- I understand that submissions cannot be completely removed once accepted.
- I understand that gnuradio.org reserves the right to reclassify or reject any submission.