University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Gaussian mixture gain priors for regularized nonnegative matrix factorization in single-channel source separation

Grais, EM and Erdogan, H (2012) Gaussian mixture gain priors for regularized nonnegative matrix factorization in single-channel source separation

Full text not available from this repository.

Abstract

We propose a new method to incorporate statistical priors on the solution of the nonnegative matrix factorization (NMF) for single-channel source separation (SCSS) applications. The Gaussian mixture model (GMM) is used as a log-normalized gain prior model for the NMF solution. The normalization makes the prior models energy independent. In NMF based SCSS, NMF is used to decompose the spectra of the observed mixed signal as a weighted linear combination of a set of trained basis vectors. In this work, the NMF decomposition weights are enforced to consider statistical prior information on the weight combination patterns that the trained basis vectors can jointly receive for each source in the observed mixed signal. The NMF solutions for the weights are encouraged to increase the loglikelihood with the trained gain prior GMMs while reducing the NMF reconstruction error at the same time.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Grais, EMUNSPECIFIEDUNSPECIFIED
Erdogan, HUNSPECIFIEDUNSPECIFIED
Date : 1 December 2012
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:54
Last Modified : 17 May 2017 13:54
URI: http://epubs.surrey.ac.uk/id/eprint/840740

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800