Probabilistic Modelling of Signal Mixtures with Differentiable Dictionaries

Abstract

We introduce a novel way to incorporate prior information into (semi-) supervised non-negative matrix factorization, which we call differentiable dictionary search. It enables general, highly flexible and principled modelling of mixtures where non-linear sources are linearly mixed. We study its behavior on an audio decomposition task, and conduct an extensive, highly controlled study of its modelling capabilities.

Published in the Proceedings of the 29th European Signal Processing Conference (EUSIPCO 2021), Dublin, Ireland, August 23-27, 2021 (IEEE), 441-445