关键词: 语音增强, 压缩感知, 分组分离, 软阈值, 去噪

Abstract: Compressed Sensing（CS） which is a signal sparsity-based sampling method, can effectively extract the information contained in the signal. A new method is designed for noisy speech enhancement based on the grouping separation of compressed sensing. Speech sparse expression is used in discrete Fast Fourier Transform（FFT） domain. The algorithm can implement compression measurement and denoising in noisy speech by the design of the complex domain observation matrix and soft threshold. Sparse Reconstruction by Separable Approximation, SpaRSA algorithm is used to restore the speech signal, to achieve speech enhancement. The experiments show that the denoising signal can be compressed and reconstructed for noise signal compression refactoring. The signal-to-noise ratio can be improved greatly. The background noise can be  more effectively suppressed.

Key words: speech enhancement, compressed sensing, group-separable, soft threshold, denoising