Neural network approach to the cocktail party effect Imagine you have 2 people at 2 different microphones but in the same room. Each microphone is going to pick up some sound from the other person. Is there a good neural network based approach to isolating the signals so that the sound from each microphone only captures 1 person?

I remember hearing a solution to this a few years back, but Im not sure if I remember that correctly

I ask because a similar problem was mentioned to me today. During EEG brain wave data collection, each electrode can pick up signal from multiple sources in the brain. In that world they try to isolate the sources and reduce the "noise" from other brain areas, and its common to use ICA for such a task. The problem with ICA is that the post-processing stage is very time consuming, so I'm wondering if theres a better ANN/DNN approach that could solve the problem more efficiently, or maybe with better accuracy
Take a look at this. 

No DNN, but math, if different channels are available.

DNN were used for single channel input, but have to be trained to the signals you want to separate.