Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Bin Gao ; Sch. of Electr., Electron., & Comput. Eng., Newcastle Univ., Newcastle upon Tyne, UK ; Woo, W.L. ; Dlay, S.S.

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.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:5 ,  Issue: 5 )