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Variational regularized two-dimensional nonnegative matrix factorization with the flexible β-Divergence for single channel source separation

Lookup NU author(s): Kaiwan Yu, Dr Wai Lok Woo, Professor Satnam Dlay


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This paper presents an algorithm for nonnegative matrix factorization 2D (NMF-2D) with the flexible β-Divergence. The β-Divergence is a group of cost functions parameterized by a single parameter β. The Least Squares divergence, Kullback-Leibler divergence and the Itakura-Saito divergence are special cases (β=2,1,0).This paper presents a more complete and holistic algorithm which uses a flexible range of β, instead of being limited to the special cases. We describe a maximization minimization (MM) algorithm lead to multiplicative updates. The proposed factorization decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral dictionary and temporal codes with enhanced performance. The method is demonstrated on the separation of audio mixtures recorded from a single channel. The method also enables a generalized criterion for variable sparseness to be imposed onto the solution. Experimental tests and comparisons with other factorization methods have been conducted to verify the efficacy of the proposed method.

Publication metadata

Author(s): Yu K, Woo WL, Dlay SS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

Year of Conference: 2015

Online publication date: 17/11/2016

Acceptance date: 01/01/1900

Publisher: Institution of Engineering and Technology


DOI: 10.1049/cp.2015.1788

Library holdings: Search Newcastle University Library for this item

Series Title: IET Conference Publications

ISBN: 9781785611360