Affiliations: [a] Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan | [b] Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan
Correspondence:
[*]
Corresponding author: Jia-Ching Wang, Department of Computer Science and Information Engineering, National Central University, Taiwan. E-mail:[email protected]
Abstract: The aim of single channel source separation is to
accurately recover signals from mixtures. Non-negative matrix factorization
(NMF) is a popular method to separate mixed signals using learned
dictionaries. These dictionaries can be produced efficiently by sparse NMF
to approximate the input signal as closely as possible. However, the
literature does not consider the structure of the data in terms of the
similarity among vertices of the input signal. Furthermore, state-of-art
variants of NMF that are more efficient than conventional ones have not been
utilized, and the learned dictionary is typically fixed in the separating
phase. This strategy is not favorable because the training data and the
testing data totally differ. To deal with these issues, our work proposes a
method that incorporates the graph regularization into group sparsity
β-NMF to improve the performance of source separation. The proposed
algorithms differ from those in the literature by using an adaptive
dictionary in which particular characteristics of the testing data are
updated to produce newer dictionaries. Experimental results demonstrate that
our proposed method is outstandingly effective in speech separation in
various scenarios, relative to the baseline.
