Probabilistic Optimization for Source Separation
Gerald Schuller, Oleg Golokolenko · 2020
We present a novel probabilistic Zeroth-Order optimization method, which can handle higher dimensions, and can also be used for fast online optimization, for instance for multichannel source separation. We compared it to the Gradientless Descent (GLD) algorithm on a multichannel source separation task, and found that our method results in faster and better separation (for the 2-channels case). For the multichannel case, only our method resulted in useful separation. We also applied it to separating sources from 3-dimensional microphone arrays, with comparable results.