4 kb/s语音编码中LPC参数的树搜索多阶段矢量量化

B. Bhattacharya, W. LeBlanc, S. Mahmoud, V. Cuperman
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引用次数: 31

摘要

提出了一种基于树搜索的多阶段矢量量化(TS-MSVQ)方法,该方法可以在24 b/帧的情况下实现低于1 dB的频谱失真,并且具有较低的复杂度和较好的鲁棒性。使用M-L搜索,结果表明,它实现的性能接近于相对较小的m的最优搜索。在3 - 4阶段配置中级联的相对较小的码本获得了最佳性能/复杂性权衡。给出了对数面积比(LAR)和线谱疼痛(LSP)参数的计算结果。介绍了一种以平均性能轻微下降为代价减少异常值的训练技术。研究了不同语言和不同输入谱形状的鲁棒性。最后,研究表明,TS-MSVQ显著优于码本分割方法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tree searched multi-stage vector quantization of LPC parameters for 4 kb/s speech coding
The authors present a tree searched multi-stage vector quantization (TS-MSVQ) scheme which achieves spectral distortion lower than 1 dB with low complexity and good robustness using 24 b/frame. The M-L search is used and it is shown that it achieves performance close to that of the optimal search for a relatively small M. The best performance/complexity trade-offs are obtained with relatively small size codebooks cascaded in a three-four stage configuration. Results for log-area ratio (LAR) and line spectral pain (LSP) parameters are presented. A training technique which reduces outliers at the expense of a slight average performance degradation is introduced. The robustness across different languages and input spectral shapings is studied. Finally, it is shown that TS-MSVQ significantly outperforms the split-codebook approach.<>
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