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Application of compressed sensing theory in the sampling and reconstruction of speech signals

机译：压缩传感理论在语音信号采样与重构中的应用

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摘要

This paper studies the application of compressed sensing theory in speech signal sampling and reconstruction of speech signals. According to the sparsity of speech signals in the discrete cosine transform basis (DCT), we propose a speech compressed sensing (CS) system based on DCT domain which realizes sparse representation of speech signal in DCT domain. Utilizing Gauss random matrix as the measurement matrix and orthogonal matching pursuit algorithm (OMP), the performance of speech signal reconstruction is acquired. The simulation results show that the sparsity of the speech signal is higher in the DCT domain and the OMP algorithm can effectively improves the performance of reconstructed speech signals.
机译：本文研究了压缩传感理论在语音信号采样中的应用和语音信号的重建。 根据离散余弦变换的基础（DCT）的语音信号的稀疏性，我们提出了一种基于DCT域的语音压缩传感（CS）系统，其实现了DCT域中语音信号的稀疏表示。 利用高斯随机矩阵作为测量矩阵和正交匹配追踪算法（OMP），获取语音信号重建的性能。 模拟结果表明，DCT域中语音信号的稀疏性较高，并且OMP算法可以有效地提高重建语音信号的性能。

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