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A comparative study of LPC parameter representations and quantisation schemes for wideband speech coding

机译:LPC参数表示和宽带语音编码量化方案的比较研究

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摘要

In this paper, we provide a review of LPC parameter quantisation for wideband speech coding as well as evaluate our contributions, namely the switched split vector quantiser (SSVQ) and multi-frame GMM-based block quantiser. We also compare the performance of various quantisation schemes on the two popular LPC parameter representations: line spectral frequencies (LSFs) and immittance spectral pairs (ISPs). Our experimental results indicate that ISPs are superior to LSFs by I bit/frame in independent quantiser schemes, such as scalar quantisers; while LSFs are the superior representation for joint vector quantiser schemes. We also derive informal lower bounds, 35 and 36 bits/frame, for the transparent coding of LSFs and ISPs, respectively, via the extrapolation of the operating distortion-rate curve of the unconstrained vector quantiser. Finally, we report and discuss the results of applying the SSVQ with dynamically-weighted distance measure and the multi-frame GMM-based block quantiser, which achieve transparent coding at 42 and 37 bits/frame, respectively, for LSFs. ISPs were found to be inferior to the LSFs by I bit/frame. In our comparative study, other quantisation schemes that were investigated include PDF-optimised scalar quantisers. the memoryless Gaussian mixture model-based block quantiser, the split vector quantiser, and the split-multistage vector quantiser with MA predictor from the AMR-WB (ITU-T G.722.2) speech coder. (c) 2005 Elsevier Inc. All rights reserved.
机译:在本文中,我们对宽带语音编码的LPC参数量化进行了回顾,并评估了我们的贡献,即开关分离矢量量化器(SSVQ)和基于多帧GMM的块量化器。我们还比较了两种流行的LPC参数表示形式上各种量化方案的性能:线谱频率(LSF)和阻抗谱对(ISP)。我们的实验结果表明,在独立的量化器方案(例如标量量化器)中,ISP在I位/帧方面优于LSF。 LSF是联合矢量量化方案的高级表示。我们还通过对无约束矢量量化器的工作失真率曲线进行外推,分别得出LSF和ISP透明编码的非正式下限,分别为35和36位/帧。最后,我们报告并讨论了将SSVQ与动态加权距离测量和基于多帧GMM的块量化器一起应用的结果,它们分别对LSF实现了42和37位/帧的透明编码。发现ISP比I位/帧差于LSF。在我们的比较研究中,研究的其他量化方案包括PDF优化的标量量化器。基于AMR-WB(ITU-T G.722.2)语音编码器的具有MA预测器的无内存基于高斯混合模型的块量化器,分割矢量量化器和分割多级矢量量化器。 (c)2005 Elsevier Inc.保留所有权利。

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