Name
Abstract | ||
|---|---|---|
Non-negative matrix factorization (NMF) is one of the most well-known techniques that are applied to separate a desired source from mixture data. In the NMF framework, a collection of data is factorized into a basis matrix and an encoding matrix. The basis matrix for mixture data is usually constructed by augmenting the basis matrices for independent sources. However, target source separation with the concatenated basis matrix turns out to be problematic if there exists some overlap between the subspaces that the bases for the individual sources span. In this letter, we propose a novel approach to improve encoding vector estimation for target signal extraction. Estimating encoding vectors from the mixture data is viewed as a regression problem and a deep neural network (DNN) is used to learn the mapping between the mixture data and the corresponding encoding vectors. To demonstrate the performance of the proposed algorithm, experiments were conducted in the speech enhancement task. The experimental results show that the proposed algorithm outperforms the conventional encoding vector estimation scheme. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2014.2354456 | IEEE Signal Process. Lett. |
Keywords | DocType | Volume |
dnn,deep neural network,basis matrix,encoding vectors estimation,target signal extraction,NMF-based target source separation,nmf-based target source separation,data factorization,dictionary learning,encoding,concatenated basis matrix,estimation theory,mixture models,mixture data,source separation,Deep neural network,encoding vector estimation,encoding matrix,nonnegative matrix factorization,matrix decomposition,speech enhancement task,DNN,non-negative matrix factorization,target source separation,speech enhancement,neural nets,vectors | Journal | 22 |
Issue | ISSN | Citations |
2 | 1070-9908 | 16 |
PageRank | References | Authors |
0.64 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Gyoon Kang | 1 | 27 | 4.42 |
| Kisoo Kwon | 2 | 35 | 3.35 |
| Jong Won Shin | 3 | 215 | 21.85 |
| Nam Soo Kim | 4 | 275 | 29.16 |