Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a frame. In the implementation, the constrained gain is e...Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a frame. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR (Signal-to-Noise Ratio). Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectrum. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA (Log Spectral Amplitude) in terms of MBSD (Modified Bark Spectral Distortion), segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR.展开更多
文摘针对高光谱图像(hyperspectral images,HSI)去条带易引起影像结构细节丢失问题,提出一种基于加权块稀疏(weighted block sparsity,WBS)正则化联合最小最大非凸惩罚(minimax concave penalty,MCP)约束的HSI去条带方法。本算法采用加权ℓ_(2),1范数和MCP范数对条带稀疏结构和低秩约束,ℓ_(1)范数对干净图像结构保持正则化约束,构建加权块稀疏和MCP约束的条带去除模型,采用交替方向乘子(alternating direction method of multipliers,ADMM)算法迭代求解对应模型,重建获得干净的HSI图像。实验结果表明,提出方法在实际HSI的平均等效视数从28.45提高到83.47,边缘保持指数较其他算法至少增加0.056,特别是对于非周期条带噪声,采用自适应权值更新稀疏水平,增强了组稀疏性,在保持影像边缘和加强区域平滑性方面性能更佳,去噪声效果更好。
基金supported by the National Natural Science Foundation of China (No.21688102, No.91436209, and No.21427804)the Chinese Academy of Science (No.XDB21020100)
基金Supported by 973 Project of China (No.2002 CB312102)and the National Natural Science Foundation of China (No.60272044).
文摘Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a frame. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR (Signal-to-Noise Ratio). Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectrum. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA (Log Spectral Amplitude) in terms of MBSD (Modified Bark Spectral Distortion), segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR.
