基于广义排序码本矢量量化的语音频谱编码

H.R.S. Mohammadi
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引用次数: 0

摘要

排序码本矢量量化(SCVQ)是一种非常有效的矢量量化方法。提出了SCVQ的泛化方法,并描述了其在语音编码中使用线谱频率量化(LSF)进行语音频谱编码的应用,LSF是表示语音编码中频谱量化的线性预测模型的最常用参数。通过计算机仿真对新方法的性能进行了评价。结果表明,该方法具有较好的质量和较低的实施成本。
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Spectral coding of speech based on generalized sorted codebook vector quantization
Sorted codebook vector quantization (SCVQ) is shown to be a very efficient vector quantization method. Generalization of SCVQ is suggested and its application to the spectral coding of speech using the quantization of line spectral frequencies (LSF), which are the most popular parameters to represent the linear prediction model for spectrum quantization in speech coders, is described. Computer simulations are conducted to evaluate the performance of the new method. We demonstrate that the new method achieves superior quality and has low implementation costs.
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