Memory-Enhanced MMSE Decoding in Vector Quantization(Speech and Hearing)

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概要

• 論文の詳細を見る

• An approach to minimum mean-squared error (MMSE) decoding for vector quantization over channels with memory is presented. The decoder is based on the Gilbert channel model that allows the exploitation of both intra- and inter-block correlation of bit error sequences. We also develop a recursive algorithm for computing the a posteriori probability of a transmitted index sequence, and illustrate its performance inquantization of Gauss-Markov sources under noisy channel con-ditions

• 社団法人電子情報通信学会の論文
• 2003-10-01

著者

• Chang Wen-Whei

  Department of Communication Engineering National Chiao Tung University

• Hsu Heng-Iang

  Department of Communication Engineering National Chiao Tung University

• Hsu Heng-iang

  Department Of Communication Engineering National Chiao-tung University

• Chang Wen-whei

  Department Of Communication Engineering National Chiao-tung University

• LIU Xiaobei

  School of Electrical and Electronic Engineering, Nanyang Technological University

• KOH Soo

  School of Electrical and Electronic Engineering, Nanyang Technological University

• Koh Soo

  School Of Electrical And Electronic Engineering Nanyang Technological University

• Liu Xiaobei

  School Of Electrical And Electronic Engineering Nanyang Technological University

関連論文

• AUTOMATED CRYPTANALYSIS OF BANDSPLITTER-BASED SPEECH SCRAMBLERS
• A Binary Channel Characterization Using Genetic Algorithm
• Memory-Enhanced MMSE Decoding in Vector Quantization(Speech and Hearing)
• Perceptual-Based Playout Mechanisms for Multi-Stream Voice over IP Networks

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