title   
  

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

Grais, Emad Mounir and Erdoğan, Hakan (2012) Gaussian mixture gain priors for regularized nonnegative matrix factorization in single-channel source separation. In: 13th Annual Conference of the International Speech Communication Association (InterSpeech 2012), Portland, Oregon, USA.

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
306Kb

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:Papers in Conference Proceedings
Uncontrolled Keywords:Nonnegative matrix factorization, single-channel source separation, and Gaussian mixture models
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:19661
Deposited By:Emad Mounir Grais
Deposited On:21 Oct 2012 23:24
Last Modified:28 Feb 2014 21:29

Repository Staff Only: item control page