本文基于语音信号在离散余弦基上的近似稀疏性,采用稀疏随机观测矩阵和线性规划重构算法对语音信号进行压缩感知与重构.研究了语音信号的压缩感知观测序列特性,根据语音帧和非语音帧压缩感知观测序列频谱幅度分布分散且差异较大的特性,提出基于压缩感知观测序列倒谱距离的语音端点检测算法,并对4dB-20dB下的带噪语音进行端点检测仿真实验.仿真结果显示,基于压缩感知观测序列倒谱距离的语音端点检测算法与奈奎斯特采样下语音的倒谱距离端点检测算法一样具有良好的抗噪性能,但由于采用压缩采样,减少了端点检测算法的运算数据量.%Based on the approximate sparsity of speech signal in discrete cosine basis, Compressed Sensing theory is applied to compress and decompress speech signal in this paper, that is, speech signal is projected to a sparse random measurement matrix and reconstructed by Linear Program. Features of compressed sensing measurements of speech signal is researched in this paper. According to the decentralization characteristic of amplitude spectrum distribution of compressed sensing; measurements of speech signal and difference of amplitude spectrum of compressed sensing measurements of voice and non-voice speech, endpoint detection algorithm based on cepstral distance of compressed sensing measurements of speech signal is proposed. Simulation with noisy speech signals ranging from 4dB to 20 dB is made. The simulation results show that endpoint detection algonthm based on cepstral distance of compressed sensing measurements can achieve the same performance of cepstral distance of speech aignal sampled by Nyquist Theory in noisy circumstance. The amount of calculation of endpoint detection of speech is reduced by the compressed sensing technology.
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