Nonnegative matrix factorization 2D with the flexible β-Divergence for single channel source separation

Yu, Kaiwen, Woo, Wai Lok and Dlay, Satnam (2015) Nonnegative matrix factorization 2D with the flexible β-Divergence for single channel source separation. In: 2015 IEEE Workshop on Signal Processing Systems (SiPS). IEEE. ISBN 978-1-4673-9604-2

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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 parametrized 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 algorithm which uses a flexible range of β, instead of be limited to just 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. 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: 10 Apr 2019 11:42
Last Modified: 10 Apr 2019 11:42

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