Vector quantization of LSP parameters using moving average interframe prediction

Hitoshi Ohmuro, Takehiro Moriya, Kazunori Mano, Satoshi Miki

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper proposes a new method for vector quantization (VQ) of LSP parameters, using moving average (MA) interframe predictions. MA predictive VQ executes the prediction using the code vectors (codebook outputs) in the previous frames. Moreover, this method has the following features, compared to autoregressive (AR) predictive coding, which executes the prediction based on the previous quantized (decoded) values: (1) even if a bit error is produced in the transmission channel, its effect to the succeeding frames remains finite; and (2) the stored codes can be decoded from any time point. This paper discusses the quantization performance and the robustness against bit errors of the MA predictive VQ, as well as the training method for the codebook. Assuming an application to low‐bit‐rate speech coding, the configuration considered has a frame length of 40 ms, with each frame composed of four subframes. A method for further improvement of the efficiency is reported. As a result of evaluation using actual speech data, bit reductions of approximately 16 percent and 23 percent are achieved for the quantization for each 20 ms and 40 ms, respectively, compared to the conventional method which does not use interframe prediction.

Original languageEnglish
Pages (from-to)12-26
Number of pages15
JournalElectronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
Volume77
Issue number10
DOIs
Publication statusPublished - 1994
Externally publishedYes

Fingerprint

Vector quantization
Speech coding

Keywords

  • bit error
  • interframe prediction
  • LSP
  • moving average
  • vector quantization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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title = "Vector quantization of LSP parameters using moving average interframe prediction",
abstract = "This paper proposes a new method for vector quantization (VQ) of LSP parameters, using moving average (MA) interframe predictions. MA predictive VQ executes the prediction using the code vectors (codebook outputs) in the previous frames. Moreover, this method has the following features, compared to autoregressive (AR) predictive coding, which executes the prediction based on the previous quantized (decoded) values: (1) even if a bit error is produced in the transmission channel, its effect to the succeeding frames remains finite; and (2) the stored codes can be decoded from any time point. This paper discusses the quantization performance and the robustness against bit errors of the MA predictive VQ, as well as the training method for the codebook. Assuming an application to low‐bit‐rate speech coding, the configuration considered has a frame length of 40 ms, with each frame composed of four subframes. A method for further improvement of the efficiency is reported. As a result of evaluation using actual speech data, bit reductions of approximately 16 percent and 23 percent are achieved for the quantization for each 20 ms and 40 ms, respectively, compared to the conventional method which does not use interframe prediction.",
keywords = "bit error, interframe prediction, LSP, moving average, vector quantization",
author = "Hitoshi Ohmuro and Takehiro Moriya and Kazunori Mano and Satoshi Miki",
year = "1994",
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AU - Ohmuro, Hitoshi

AU - Moriya, Takehiro

AU - Mano, Kazunori

AU - Miki, Satoshi

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N2 - This paper proposes a new method for vector quantization (VQ) of LSP parameters, using moving average (MA) interframe predictions. MA predictive VQ executes the prediction using the code vectors (codebook outputs) in the previous frames. Moreover, this method has the following features, compared to autoregressive (AR) predictive coding, which executes the prediction based on the previous quantized (decoded) values: (1) even if a bit error is produced in the transmission channel, its effect to the succeeding frames remains finite; and (2) the stored codes can be decoded from any time point. This paper discusses the quantization performance and the robustness against bit errors of the MA predictive VQ, as well as the training method for the codebook. Assuming an application to low‐bit‐rate speech coding, the configuration considered has a frame length of 40 ms, with each frame composed of four subframes. A method for further improvement of the efficiency is reported. As a result of evaluation using actual speech data, bit reductions of approximately 16 percent and 23 percent are achieved for the quantization for each 20 ms and 40 ms, respectively, compared to the conventional method which does not use interframe prediction.

AB - This paper proposes a new method for vector quantization (VQ) of LSP parameters, using moving average (MA) interframe predictions. MA predictive VQ executes the prediction using the code vectors (codebook outputs) in the previous frames. Moreover, this method has the following features, compared to autoregressive (AR) predictive coding, which executes the prediction based on the previous quantized (decoded) values: (1) even if a bit error is produced in the transmission channel, its effect to the succeeding frames remains finite; and (2) the stored codes can be decoded from any time point. This paper discusses the quantization performance and the robustness against bit errors of the MA predictive VQ, as well as the training method for the codebook. Assuming an application to low‐bit‐rate speech coding, the configuration considered has a frame length of 40 ms, with each frame composed of four subframes. A method for further improvement of the efficiency is reported. As a result of evaluation using actual speech data, bit reductions of approximately 16 percent and 23 percent are achieved for the quantization for each 20 ms and 40 ms, respectively, compared to the conventional method which does not use interframe prediction.

KW - bit error

KW - interframe prediction

KW - LSP

KW - moving average

KW - vector quantization

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