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

Yu, Kaiwen, Woo, Wai Lok and Dlay, Satnam (2016) Variational regularized two-dimensional nonnegative matrix factorization with the flexible β-divergence for single channel source separation. In: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). IEEE. ISBN 978-1-78561-136-0

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Abstract

This paper presents an algorithm for nonnegative matrix factorization 2D (NMF-2D) with the flexible ß-Divergence. The ß-Divergence is a group of cost functions parameterized by a single parameter ß. The Least Squares divergence, Kullback-Leibler divergence and the Itakura-Saito divergence are special cases (ß=2,1,0).This paper presents a more complete and holistic algorithm which uses a flexible range of ß, 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.

Item Type:	Book Section
Uncontrolled Keywords:	Single channel source separation, audio processing, non-negative matrix factorization, β-Divergence, maximization-minimization
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	11 Apr 2019 09:32
Last Modified:	10 Oct 2019 20:15
URI:	http://nrl.northumbria.ac.uk/id/eprint/38929

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