Sparse Representations for Speech Enhancement  

作　　者：ZHAO Nan XU Xin YANG Yi 

机构地区：[1]Signal Processing Laboratory, School of Electronic Information, Wuhan University, Wuhan 530079, China

出　　处：《Chinese Journal of Electronics》2011年第2期268-272,共5页电子学报（英文版）

摘　　要：This paper applies the sparse and redundant representation techniques to the problem of speech enhancement. More specifically, the K-SVD algorithm was used to train a data-driven overcomplete dictionary that describes the sparsity of speech. Orthogonal matching pursuit was employed to reconstruct the clean speech as a direct sparse decomposition technique over redundant dictionaries. Furthermore, the principle of iteration was introduced to the denoising process. When training was done on the noisy speech directly, the overall trainingreconstructing algorithm became fused into one iterative procedure. Simulation shows that our proposed approach outperforms the conventional methods in terms of spectrogram analysis, objective and subjective measures.

关 键 词：Speech enhancement SPARSITY Redun- dant representations K-singular value decomposition （K- SVD） Orthogonal matching pursuit. 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]