Adaptive sparsity non-negative matrix factorization for single-channel source separation

Bin Gao*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

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 with adaptive sparsity which subsequently leads to better source separation performance. Experimental tests and comparisons with other sparse factorization methods have been conducted to verify the efficacy of the proposed method.

Original languageEnglish
Article number5934578
Pages (from-to)989-1001
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Volume5
Issue number5
DOIs
Publication statusPublished - 27 Jun 2011

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