Name
Abstract | ||
|---|---|---|
In this letter, we propose a novel speech enhancement technique based on global soft decision incorporating a support vector machine (SVM). Global soft decision in the proposed approach is performed employing the probabilistic outputs of the SVM rather than the conventional Bayes' rule. Actually, global speech absence probability (GSAP) is determined by the sigmoid function based on key parameters... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/LSP.2008.2008574 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Support vector machines,Speech enhancement,Additive noise,Signal to noise ratio,Signal processing algorithms,Discrete Fourier transforms,Minimization methods,Degradation,Industrial training,Frequency | Speech enhancement,Noise measurement,Pattern recognition,Computer science,Speech quality,Support vector machine,Speech recognition,Artificial intelligence,Probabilistic logic,Minimization algorithm,Sigmoid function,Bayes' theorem | Journal |
Volume | Issue | ISSN |
16 | 1 | 1070-9908 |
Citations | PageRank | References |
8 | 0.64 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joon-Hyuk Chang | 1 | 263 | 21.87 |
| Q-Haing Jo | 2 | 26 | 2.32 |
| Dong Kook Kim | 3 | 50 | 9.44 |
| Nam Soo Kim | 4 | 275 | 29.16 |