Hidden Mmarkov models as priors for regularized nonnegative matrix factorization in single-channel source separation

Grais, EM and Erdogan, H (2012) Hidden Mmarkov models as 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 rich statistical priors, modeling temporal gain sequences in the solutions of nonnegative matrix factorization (NMF). The proposed method can be used for single-channel source separation (SCSS) applications. 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 and temporal prior information on the weight combination patterns that the trained basis vectors can jointly receive for each source in the observed mixed signal. The Hidden Markov Model (HMM) is used as a log-normalized gains (weights) prior model for the NMF solution. The normalization makes the prior models energy independent. HMM is used as a rich model that characterizes the statistics of sequential data. The NMF solutions for the weights are encouraged to increase the log-likelihood with the trained gain prior HMMs while reducing the NMF reconstruction error at the same time.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Surrey research (other units)
Authors :	Grais, EM and Erdogan, H
Date :	1 December 2012
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 13:54
Last Modified :	23 Jan 2020 18:56
URI:	http://epubs.surrey.ac.uk/id/eprint/840741

Actions (login required)

View Item

Downloads