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.
- Graph regularization
- adaptive dictionary learning
- non-negative matrix factorization
- source separation