Mixtures of Gamma Priors for Non-negative Matrix Factorization Based Speech Separation
In this paper we present a novel approach for isolating and removing sounds from dense monophonic mixtures. The approach is user-based, and requires the presentation of a guide sound that mimics the desired target the user wishes to extract. The guide sound can be simply produced from a user by vocalizing or otherwise replicating the target sound marked for separation. Using that guide as a prior in a statistical sound mixtures model, we propose a methodology that allows us to efficiently extract complex structured sounds from dense mixtures.