Neural Beamforming: Where Classical DSP Ends and Learned Spatial Filtering Begins

Contents

What classical beamforming actually solves for
Where the stationarity assumption breaks
What a neural network actually changes
Why not go fully neural end to end
Where the training data comes from, and why it's a limiting factor
The geometry ceiling still applies
Where this leaves the design