ISCA Archive Interspeech 2015
ISCA Archive Interspeech 2015

Regularized non-negative matrix factorization using alternating direction method of multipliers and its application to source separation

Shaofei Zhang, Dong-Yan Huang, Lei Xie, Eng Siong Chng, Haizhou Li, Minghui Dong

Non-negative matrix factorization (NMF) aims at finding non-negative representations of nonnegative data. Among different NMF algorithms, alternating direction method of multipliers (ADMM) is a popular one with superior performance. However, we find that ADMM shows instability and inferior performance on real-world data like speech signals. In this paper, to solve this problem, we develop a class of advanced regularized ADMM algorithms for NMF. Efficient and robust learning rules are achieved by incorporating l1-norm and the Frobenius norm regularization. The prior information of Laplacian distribution of data is used to solve the problem with a unique solution. We evaluate this class of ADMM algorithms using both synthetic and real speech signals for a source separation task at different cost functions, i.e., Euclidean distance (EUD), Kullback-Leibler (KL) divergence and Itakura-Saito (IS) divergence. Results demonstrate that the proposed algorithms converge faster and yield more stable and accurate results than the original ADMM algorithm.


doi: 10.21437/Interspeech.2015-356

Cite as: Zhang, S., Huang, D.-Y., Xie, L., Chng, E.S., Li, H., Dong, M. (2015) Regularized non-negative matrix factorization using alternating direction method of multipliers and its application to source separation. Proc. Interspeech 2015, 1498-1502, doi: 10.21437/Interspeech.2015-356

@inproceedings{zhang15b_interspeech,
  author={Shaofei Zhang and Dong-Yan Huang and Lei Xie and Eng Siong Chng and Haizhou Li and Minghui Dong},
  title={{Regularized non-negative matrix factorization using alternating direction method of multipliers and its application to source separation}},
  year=2015,
  booktitle={Proc. Interspeech 2015},
  pages={1498--1502},
  doi={10.21437/Interspeech.2015-356},
  issn={2958-1796}
}