Title of Paper:Adaptive Sparsity Non-negative Matrix Factorization for Single Channel Source Separation

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Journal:IEEE the Journal of Selected Topics in Signal Processing

Abstract:A novel method for adaptive sparsity non-negative matrix factorization is proposed. The proposed factorization decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral dictionary and temporal codes. We derive a variational Bayesian approach to compute the sparsity parameters for optimizing the matrix factorization. The method is demonstrated on separating audio mixtures recorded from a single channel. In addition, we have proven that the extraction of the spectral dictionary and temporal codes is significantly more efficient.

All the Authors: S.S. Dlay, W.L. Woo,Bin Gao

Correspondence Author:Bin Gao

Discipline:Engineering

Volume:5

Issue:5

Page Number:989-1001

Translation or Not:no

Date of Publication:2011-08-17