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Speech Enhancement Using Compressed Sensing-based method

机译:使用基于压缩感知的方法进行语音增强

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A procedure based on compressed sensing (CS) is proposed for speech enhancement. The latter is Prior-noise-estimation free. This approach is motivated by the fact that CS allows the recovery of only the sparse signal in the presence of non-sparse signal (noise). An application to an Arabic speech signal corrupted with white Gaussian noise is studied. Comparison of CS-based enhancement with three state-of-the-art methods is performed in terms of segmental SNR, root mean square error, and perceptual evaluation of speech quality (PESQ); and proved promising performances.
机译:提出了一种基于压缩感知(CS)的过程,用于语音增强。后者没有先验噪声估计。这种方法的动机是CS在存在非稀疏信号(噪声)的情况下仅允许稀疏信号的恢复。研究了一种被高斯白噪声破坏的阿拉伯语音信号的应用。在分段SNR,均方根误差和语音质量感知评估(PESQ)方面,将基于CS的增强与三种最新方法进行了比较。并证明了良好的表现。

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