Name
Title | ||
|---|---|---|
A Statistical Model-Based Speech Enhancement Using Acoustic Noise Classification For Robust Speech Communication |
Abstract | ||
|---|---|---|
In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1587/transcom.E95.B.2513 | IEICE TRANSACTIONS ON COMMUNICATIONS |
Keywords | Field | DocType |
statistical model-based speech enhancement, Gaussian mixture model, noise classification | Noise,Speech enhancement,Noise classification,Speech communication,Pattern recognition,Computer science,Speech recognition,Statistical model,Artificial intelligence,Gaussian noise,Mixture model | Journal |
Volume | Issue | ISSN |
E95B | 7 | 0916-8516 |
Citations | PageRank | References |
3 | 0.40 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Hun Choi | 1 | 29 | 5.57 |
| Joon-Hyuk Chang | 2 | 3 | 0.40 |