[geokon]

Does anyone have any insight into why neural nets are used for the "blind" beamforming? I don't have first hand experience with machine learning, but this just doesn't seem to me like a machine learning type of problem. I get it's not trivial, but it seems like there should be an analytic solution - more or less

[crazygringo]

Acoustics are modified in extremely non-linear ways depending on the shape of the room, bodies within it, materials, acoustic reflection, acting differently at different frequencies, and so on.

In theory if the entire 3D layout and material properties were known known in advance you could get clear audio analytically. But reverse-engineering the 3D layout and materials from existing audio is essentially impossible.

So machine learning is used to find approximate solutions that work.