中文摘要：

压缩感知（Compressed Sensing,CS）理论突破了经典采样定理的理论边界,为信号压缩提供了另一种途径。基于CS理论框架,做了两方面工作：为提高语音字典对信号的匹配性,设计了一种基于K-L展开的非相干语音字典;针对现有匹配追踪（MP,OMP）算法的不足,提出分段匹配追踪（Segment MP,SegMP）算法。首先对语音自相关函数进行建模并估计模型参数,构造语音自适应非相干字典,然后采用SegMP对语音稀疏向量分段观测,获得多个低维矢量,最后结合模型参数重建字典并重构信号,实现了语音压缩感知。语音测试结果表明：相比现有方案,本文方案对信号的稀疏表示更为精准,具有更好的重构质量,且降低了计算复杂度。

英文摘要：

Compressed Sensing（CS）,an emerging theory,provides an alternative approach for signal compression.In this study,an incoherent speech dictionary scheme is proposed via K-L Expansion to improve the matching property of previous ones,and the Segment MP（SegMP） algorithm is designed aiming at the shortages of MP and OMP.We build a speech autocorrelation model and estimate the model parameter to construct the dictionary.Afterwards,the SegMP is employed to obtain low-dimensional measurements and to reconstruct speech with the dictionary that is rebuilt by the model parameter.Finally,the compressed speech signal sensing is implemented.Extensive experiments demonstrate that the presented scheme outperforms state-of-the-art method in vocal signals＇ sparse description.It has three characteristics：better signal adaptability,higher reconstruction quality and lower complexity.