网格码激励线性预测(TCELP)语音编码

Cheng-Chieh Lee, Y. Shoham
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引用次数: 1

摘要

本文介绍了用基于网格的有内存源标量矢量量化器来解决码激励线性预测(CELP)语音编码器的激励码本搜索问题。这种方法产生了24 kbit/s的电话带宽低延迟(3 msec)栅格CELP编码器,其性能优于ITU-T 15 kbit/s G.728 LD-CELP和G.726 32 kbit/s ADPCM。由于码本来源于标量字母表,因此所提出的编码器可以有效地处理24维空间中的激励矢量(实现可观的矢量量化增益),其计算复杂度约为ITU-T G.728 LD-CELP的75%。
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Trellis code excited linear prediction (TCELP) speech coding
This paper describes using the trellis-based scalar-vector quantizer for sources with memory to solve the excitation codebook search problem of code excited linear prediction (CELP) speech coders. This approach leads to a 24 kbit/s telephony-bandwidth low-delay (3 msec) trellis CELP coder, which outperforms both ITU-T 15 kbit/s G.728 LD-CELP and G.726 32 kbit/s ADPCM. Since the codebook is derived from a scalar alphabet, the proposed coder can effectively handle excitation vectors in the 24-dimensional space (to realize considerable vector quantization gains) and has a computational complexity of approximately 75% of that of ITU-T G.728 LD-CELP.
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