Adaptive Sparsity Non-Negative Matrix Factorization for Single-Channel Source Separation

Gao, Bin, Woo, Wai Lok and Dlay, Satnam (2011) Adaptive Sparsity Non-Negative Matrix Factorization for Single-Channel Source Separation. IEEE Journal of Selected Topics in Signal Processing, 5 (5). pp. 989-1001. ISSN 1932-4553

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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.

Item Type: Article
Uncontrolled Keywords: Audio processing, non-negative matrix factorization (NMF), single-channel source separation, sparse features
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 10 May 2019 11:39
Last Modified: 10 Oct 2019 19:15

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