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首页> 外文期刊>IEEE Transactions on Acoustics, Speech, and Signal Processing >Design and performance of trellis vector quantizers for speech signals
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Design and performance of trellis vector quantizers for speech signals

机译:语音信号网格矢量量化器的设计和性能

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摘要

Design algorithms are presented for linear predictive vector quantizers with trellis structures that, when combined with appropriate search procedures, can achieve lower distortions than conventional memoryless vector quantizers. Two types of trellis structures, shift register and minimum degradation network, are considered. In the shift register case (SR-TVQ), the state of the trellis encoder corresponds to the content of the shift register; it therefore does not require an explicit state-transition matrix. The minimum degradation network is a state-transition matrix obtained through a pruning procedure (P-TVQ) to give minimum distortion degradation for an omnisearch (full-rate) vector quantizer. The role of the search procedure, the delay requirements of each type of encoder, and the performance of the designs are discussed and the two methods are compared.
机译:提出了具有网格结构的线性预测矢量量化器的设计算法,当与适当的搜索过程结合使用时,与传统的无记忆矢量量化器相比,可以实现更低的失真。考虑了两种类型的网格结构,即移位寄存器和最小降级网络。在移位寄存器的情况下(SR-TVQ),网格编码器的状态对应于移位寄存器的内容;因此,它不需要显式的状态转换矩阵。最小降级网络是通过修剪过程(P-TVQ)获得的状态转换矩阵,可为全搜索(全速率)矢量量化器提供最小的失真降级。讨论了搜索过程的作用,每种编码器的延迟要求以及设计的性能,并比较了这两种方法。

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