Speaker
Description
TorchSig, an open-source signal processing machine learning (ML) library, is expanding into a reproducible Radio Frequency Machine Learning (RFML) framework with its most recent v2.x releases. We present an interactive workshop that covers: v2.x updates, new libraries torchsig-models and torchsig-gui, and new geolocation capabilities.
TorchSig v2.x updates include a complete restructure of the signal generation process, hierarchical metadata relationships, improved dataset writing, new dataset utilities, and structured signal generation. A significant new feature of TorchSig is its new geolocation tools–leveraging TorchSig's data generation for geospatial emitter simulation. We also showcase two new libraries, torchsig-models and torchsig-gui. TorchSig Models lets users create a full RFML pipeline, from creating synthetic datasets, augmenting and transforming data, to training a PyTorch-based detector. Additionally, we will demonstrate the TorchSig GUI, which provides an easy-to-use interface for creating TorchSig datasets.
Overall, the workshop aims to showcase ways RF engineers, ML practitioners, and researchers can use TorchSig for reproducible data generation, model development, and evaluation.
See the workshop materials here. See the workshop Github here.
| Talk Length | N/A |
|---|---|
| Link to Open Source Code | https://github.com/TorchDSP/torchsig |
| Acknowledge | Acknowledge In-Person |