Name
Abstract | ||
|---|---|---|
Optimal vector quantization of variable-dimension vectors in principle is feasible by using a set of fixed dimension VQ codebooks. However, for typical applications, such a multi-codebook approach demands a grossly excessive and impractical storage and com- putational complexity. Efficient quantization of such variable-dimension spectral shape vectors is the most challenging and difficult encoding task required in an important fam- ily of low bit-rate vocoders. We introduce a simple and effective formulation of variable-dimension vector quantization (VDVQ) which quantizes variable-dimension vectors using a single universal codebook having fixed dimension yet covering the entire range of input vector dimensions under consideration. This VDVQ technique is applied to quantize variable-dimension spectral shape vectors leading to a high quality speech coder at the low bit-rate of 2.5 kb/s. The combination of a universal spectral codebook and structured VQ reduces storage and computational complexity, yet delivers a high quantization efficiency and enhanced perceptual quality of the coded speech. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1109/DCC.1994.305949 | Data Compression Conference |
Keywords | Field | DocType |
speech intelligibility,computational complexity,information processing,vector quantization,application software,encoding,speech coding,speech processing,spectral shape,random variables | Speech coding,Pattern recognition,Linde–Buzo–Gray algorithm,Computer science,Vector quantization,Artificial intelligence,Quantization (signal processing),Linear predictive coding,Codebook,Computational complexity theory,Encoding (memory) | Conference |
Citations | PageRank | References |
11 | 1.67 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Das | 1 | 11 | 1.67 |
| Ajit V. Rao | 2 | 197 | 20.50 |
| Allen Gersho | 3 | 2031 | 624.48 |