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Opensourced ML Signals Toolkit


This content originally appeared on DEV Community and was authored by Isaiah Harville

Hey, I just wanted to introduce my opensourced project I’ve been working on — SigKit. SigKit is basically a toolbox of building-blocks for anyone who wants to play with real-world digitalized analog signals and machine learning without stitching together a dozen custom scripts. Under the hood you get:

  • Core types like Signal, Impairment and Modem so you think in baseband, not in arrays of floats.
  • NumPy operations for things like AWGN, phase/frequency shifts, filtering and SNR/BER calculators.
  • PyTorch Transforms that slot right into your Compose pipeline—so adding noise or fading to every sample in your data loader is a one-liner.
  • A PyTorch Lightning training + evaluation pipeline, complete with a pretrained modulation-classifier. Training your own custom ML model is as simple as running a script.
  • Dataset classes and synthetic signal generators so you never have to hand-craft a CSV of complex IQ samples.
  • (WIP) GNURadio blocks wrapping all of the above, for dropping into a live SDR flowgraph.

Where it can be used

  • Research labs & coursework: Teaching digital-comm concepts? SigKit turns abstract equations into hands-on Jupyter demos—generate, impair, plot, repeat.
  • Modulation classification: Training a neural net that actually generalizes over-the-air (instead of “works on simulated data only”).
  • SDR prototyping: Need to bounce a signal through realistic channel models before you hit the hardware? Plug in Rayleigh fading, resampling or IQ-imbalance transforms.
  • Hackathons & demos: Spin up a quick notebook that shows off “live” impairments and classification at different SNRs—no C++ or gnuradio-block coding required.
  • Synthetic data generation: When you need thousands of labeled IQ traces for ML, but you don’t have a tone-generator farm or unlimited SDRs.

In short, if you’ve ever wished for a toolkit that treats signals more like images in PyTorch—letting you compose transforms, datasets, metrics and models in one ecosystem—SigKit has your back.


This content originally appeared on DEV Community and was authored by Isaiah Harville