基于线性预测分析和差分变换的语音信号压缩感知

在压缩感知研究中，信号在不同变换下的稀疏域好坏是影响信号重构性能的重要因素。该文基于语音信号的线性预测分析(LPC)，提出一种结合了LPC分析和差分变换的语音稀疏化联合变换方法，通过正交匹配追踪算法(OMP)优化算法重构语音信号，与FFT和LPC两种稀疏化方法进行了对比分析。实验表明，在压缩比大于0.4时，联合变换法重构的语音信号性能明显优于另外两种方法。也即在相同重构性能并兼顾语音质量的情况下，联合变换法具有较小的压缩比，因而具有较好的压缩性能。采用PESQ语音质量评测方法对3种稀疏化算法重构的语音进行平均意见值(MOS)对比，联合变换法也具有较好的性能。

Compressed Sensing of Speech-Signals Based on Linear Prediction Coefficients and Difference Transformation

On the research of compressed sensing,the sparse field by certain transformations is one of the most important factors on signal reconstruction.This paper presents a new united sparsity method based on Linear Prediction Coefficients(LPC) of speech signals,which associates LPC analysis with difference transform method.Orthogonal Matching Pursuit(OMP) algorithm is used to reconstruct the speech signal,and the reconstruction performance by this new method is compared with FFT and LPC.Experiments show that,when the compression ratio is larger than 0.4,the performance of reconstructed signal by united method is much better than the other two.Namely,when the reconstruction performance of the three methods is same,the compression ratio of the united method is less than that of the two,which means the united method has better compression performance.PESQ is used to evaluate the quality of reconstructed speech,and the speech reconstructed by the united method has the higher scores.