Julius implements common Digital Signal Processing operations including resampling, FFT convolutions, and filter banks, all in PyTorch so they remain differentiable and GPU-compatible. This enables signal processing operations to be incorporated directly into neural network training pipelines. The library supports TorchScript export and focuses on efficient computation for audio processing workflows.