Name
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is a matrix factorization technique that might find meaningful latent nonnegative components. Since, however, the objective function is non-convex, the source separation performance can degrade when the iterative update of the basis matrix is stuck to a poor local minimum. Most of the research updates basis iteratively to minimize certain objective function with random initialization, although a few approaches have been proposed for the systematic initialization of the basis matrix such as the singular value decomposition. In this paper, we propose a novel basis estimation method inspired by the similarity of the bases training with the vector quantization, which is similar to Linde-Buzo-Gray algorithm. Experiments of the audio source separation showed that the proposed method outperformed the NMF using random initialization by about 1 : 6 4 dB and 1 : 4 3 dB in signal-to-distortion ratio when its target sources were speech and violin, respectively. |
Year | Venue | Field |
|---|---|---|
2016 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Singular value decomposition,Algorithm design,Pattern recognition,Matrix (mathematics),Matrix decomposition,Vector quantization,Non-negative matrix factorization,Artificial intelligence,Initialization,Mathematics,Source separation |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kisoo Kwon | 1 | 35 | 3.35 |
| Jong Won Shin | 2 | 215 | 21.85 |
| In Kyu Choi | 3 | 2 | 2.06 |
| Hyung Yong Kim | 4 | 0 | 1.01 |
| Nam Soo Kim | 5 | 275 | 29.16 |