Name
Abstract | ||
|---|---|---|
In this paper, we investigate the denoising properties of ro- bust vector quantization of the speech spectrum parameters in combination with a Kalman lter . The underlying assump- tion is that the high-energy speech regions can be used to re- construct the low-energy regions destroyed by noise. This can be achieved through vector quantization with a properly weighted distortion measure. The performance of the pro- posed system, Kalman ltering with prior vector quantiza- tion, is compared with existing schemes for parameter esti- mation used in Kalman ltering. The results indicate signif- icant improvement over the reference systems in both objec- tive and subjective tests. |
Year | Venue | Field |
|---|---|---|
2004 | Vienna | Noise reduction,Signal processing,Speech spectrum,Pattern recognition,Kalman filter,Vector quantization,Artificial intelligence,Estimation theory,Quantization (signal processing),Distortion,Mathematics |
DocType | ISBN | Citations |
Conference | 978-320-0001-65-7 | 2 |
PageRank | References | Authors |
0.49 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Volodya Grancharov | 1 | 73 | 7.39 |
| Sriram Srinivasan | 2 | 379 | 27.92 |
| Jonas Samuelsson | 3 | 165 | 11.19 |
| W. Bastiaan Kleijn | 4 | 1110 | 106.92 |