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

Kaiwen Yu, W. L. Woo, S. S. Dlay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationElectronic Proceedings of the 2015 IEEE International Workshop on Signal Processing Systems, SiPS 2015
PublisherIEEE
ISBN (Electronic)9781467396042
DOIs
Publication statusPublished - 3 Dec 2015
EventIEEE International Workshop on Signal Processing Systems, SiPS 2015 - Hangzhou, China
Duration: 14 Oct 201516 Oct 2015

Conference

ConferenceIEEE International Workshop on Signal Processing Systems, SiPS 2015
CountryChina
CityHangzhou
Period14/10/1516/10/15

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