Name
Abstract | ||
|---|---|---|
Source coding based on Gaussian Mixture Models (GMM) has been recently proposed for LPC quantization. We address in this paper the related problem of designing efficient codebooks for Gaussian vector sources. A new technique of ellipsoidal lattice vector quanti- zation (VQ) is described, based on 1) scalar companding optimized for Gaussian random variables and 2) rectangular lattice codebooks with fast trellis-based nearest neighbor search. The Barnes-Wall lattice Λ 16 in dimension 16 is applied to quantize the line spec- trum frequencies (LSF) of wideband speech signals. The LSF are computed in a manner similar to the AMR-WB speech coding algo- rithm. The performance of memoryless and predictive LSF quanti- zation for different GMM orders (4, 8 and 16) is evaluated at 36 and 46 bits per frame. The companded lattice VQ is shown to perform better than its scalar counterpart, with similar complexity. |
Year | Venue | Field |
|---|---|---|
2004 | EUSIPCO | Wideband audio,Pattern recognition,Scalar (physics),Algorithm,Parametric statistics,Gaussian,Companding,Artificial intelligence,Quantization (signal processing),Nearest neighbor search,Mixture model,Mathematics |
DocType | Citations | PageRank |
Conference | 1 | 0.39 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| marie oger | 1 | 12 | 2.82 |
| stephane ragot | 2 | 30 | 4.98 |
| Lefebvre, R. | 3 | 93 | 18.55 |