isaiah-harville/SigKit
A DSP Toolkit for PyTorch
This toolkit helps engineers and researchers working with wireless communication signals to simulate real-world signal impairments and build machine learning models for signal classification. You can generate synthetic radio signals, apply various distortions like noise or fading, and then use these to train models that can accurately identify different signal types, even over the air. It's ideal for anyone developing or testing radio frequency (RF) systems and algorithms.
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Use this if you need to generate realistic wireless signal data and build robust machine learning models for tasks like modulation classification in a software-defined radio or communication system.
Not ideal if you are looking for a standalone hardware-in-the-loop testing solution or a tool specifically for real-time spectrum analysis of live signals.
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Language
Python
License
MIT
Category
Last pushed
Jul 02, 2025
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