Name
Abstract | ||
|---|---|---|
Nonlinear predictive split vector quantization (NPSVQ) and classified NPSVQ (CNPSVQ) are introduced to exploit the correlation among the speech spectral parameters from two adjacent analysis frames. By interleaving intraframe SVQ with forward predictive SVQ, the error propagation is limited to at most one adjacent frame. At an overall bit rate of about 21 bits/frame, NPSVQ can provide similar coding quality as intraframe SVQ at 24 bits/frame. Voicing classification as used in CNPSVQ to obtain an additional average gain of 1 bit/frame for unvoiced frames. Therefore, an overall bit rate of 20 bits/frame is obtained for unvoiced frames. The particular form of nonlinear prediction we use incurs virtually no additional encoding computational complexity. We have verified our comparative performance results using subjective listening tests. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/ICASSP.1996.543232 | ICASSP |
Keywords | Field | DocType |
intraframe svq,adjacent analysis frame,nonlinear predictive split vector,adjacent frame,unvoiced frame,classified nonlinear predictive vector,overall bit rate,predictive svq,additional average gain,speech spectral parameter,additional encoding computational complexity,classified npsvq,speech processing,error propagation,correlation,speech coding,speech synthesis,computational complexity,encoding,vector quantization | Speech coding,Propagation of uncertainty,Pattern recognition,Computer science,Vector quantization,Artificial intelligence,Harmonic Vector Excitation Coding,Linear predictive coding,Interleaving,Computational complexity theory,Encoding (memory) | Conference |
ISBN | Citations | PageRank |
0-7803-3192-3 | 6 | 0.53 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. H. Y. Loo | 1 | 6 | 0.53 |
| Wai-Yip Chan | 2 | 6 | 0.53 |
| P. Kabal | 3 | 374 | 47.49 |