南京邮电大学学报（自然科学版）2012，Vol.32Issue(2)：64-68,5.
基于小波分解的语音自适应压缩感知
Adaptive Speech Compressed Sensing Based on Wavelet Transform
摘要
Abstract
Based on the characteristics coefficients of speech signal at low frequency and high frequency after wavelet transformation, this paper proposes adaptive speech compressed sensing. First, the trained overcomplete dictionary is applied to the low frequency coefficients after wavelet transformation to decrease the computation of the sparse decomposition. Second, an improved adaptive sensing matrix is proposed, which is applied to the low frequency and high frequency wavelet transformation respectively. At last, OMP reconstruct algorithm is employed to reconstruct the wavelet transformation coefficients, and then the signal can be finally recovered through Wavelet synthesis. Simulation results demonstrate that, based on wavelet transform,the approach using the adaptive speech compressed sensing has a good performance in reconstruction.关键词
压缩感知/小波分解/K-SVD/稀疏性Key words
compressed sensing/wavelet transform/K-SVD/sparsity分类
信息技术与安全科学引用本文复制引用
唐力..基于小波分解的语音自适应压缩感知[J].南京邮电大学学报（自然科学版）,2012,32(2):64-68,5.基金项目
国家重点基础研究发展计划(973计划)(2011 CB302903)和国家自然科学基金(60971129)资助项目 （973计划）
