数据采集与处理2011，Vol.26Issue(2)：123-127,5.
基于隐马尔可夫模型的能量参数预测量化算法
HMM-Based Prediction and Quantization Algorithms for Energy Parameters
摘要
Abstract
To use the correlation between energy parameters and linear prediction coding (LPC) coefficients, and to quantize the energy parameters more efficiently, hidden Markov model (HMM) based prediction and quantization algorithms are proposed.HMM is used to model the correlation between the energy and the LPC coefficients under appropriate assumptions.In HMM, the discretized energy parameters constitute hidden state sequences and the quantized LPC coefficients constitute observation sequences.HMM is used to predict the energy contour of each super frame, and then mode-based vector quantization (MBQ) is applied to quantize the energy prediction errors according to the predicted energy contour.Experimental result shows that the average quantization distortion is 2.668 dB, which is reduced by 14.0％ comparing with linear prediction and quantization algorithms.It implies that the proposed algorithms can improve the energy quantization efficiency by using the correlation between energy parameters and LPC coefficients.关键词
语音编码/低速率/隐马尔可夫模型/分模式量化分类
信息技术与安全科学引用本文复制引用
魏旋,计哲,崔慧娟,唐昆..基于隐马尔可夫模型的能量参数预测量化算法[J].数据采集与处理,2011,26(2):123-127,5.基金项目
国家自然科学基金(60572081)资助项目. （60572081）
