Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discusse...Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper. The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed program and Basis Pursuit (BP). The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals. It is proved by the simulation results of the mixed program which is an improved version of the mixed program. The second one is the well known large DCT coefficients of voiced speech focus on low frequency. In line with this feature, a special Gaussian and Partial Identity Joint (GPIJ) matrix is constructed as the sensing matrix for voiced speech signals. Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.展开更多
语音信号是一种非稳态的随机信号,而传统的时频分析法缺乏对这类信号进行最稀疏表示的能力,为此提出了一种完备的局部均值分解(Ensemble Local Mean Decomposition,ELMD)联合粒子群优化小波阈值语音消噪分析法:在对原始信号LMD(局部均...语音信号是一种非稳态的随机信号,而传统的时频分析法缺乏对这类信号进行最稀疏表示的能力,为此提出了一种完备的局部均值分解(Ensemble Local Mean Decomposition,ELMD)联合粒子群优化小波阈值语音消噪分析法:在对原始信号LMD(局部均值分解,Local Mean Decomposition)分解基础上加入高斯白噪声辅助分析的自适应分析法,以减轻分解后的产生模态混叠现象;对于分解后的分量中残留的噪声使用粒子群优化算法获得最优小波阈值滤除。对实际采集语音信号进行Matlab仿真的处理分析结果显示,该算法在抑制语音中的背景噪声有着良好的效果,且有效降低了对语音有效信息的损伤。展开更多
In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine ...In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60971129)the National Research Program of China (973 Program) (No. 2011CB302303)the Scientific Innovation Research Program of College Graduate in Jiangsu Province (No. CXLX11_0408)
文摘Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS). Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper. The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed program and Basis Pursuit (BP). The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals. It is proved by the simulation results of the mixed program which is an improved version of the mixed program. The second one is the well known large DCT coefficients of voiced speech focus on low frequency. In line with this feature, a special Gaussian and Partial Identity Joint (GPIJ) matrix is constructed as the sensing matrix for voiced speech signals. Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.
文摘语音信号是一种非稳态的随机信号,而传统的时频分析法缺乏对这类信号进行最稀疏表示的能力,为此提出了一种完备的局部均值分解(Ensemble Local Mean Decomposition,ELMD)联合粒子群优化小波阈值语音消噪分析法:在对原始信号LMD(局部均值分解,Local Mean Decomposition)分解基础上加入高斯白噪声辅助分析的自适应分析法,以减轻分解后的产生模态混叠现象;对于分解后的分量中残留的噪声使用粒子群优化算法获得最优小波阈值滤除。对实际采集语音信号进行Matlab仿真的处理分析结果显示,该算法在抑制语音中的背景噪声有着良好的效果,且有效降低了对语音有效信息的损伤。
基金Supported by the Naltural Science Foundation of Hunan Province(97JJY1006)Open Foundation of Stalte Key Lab. of Theory and Chief Technology on ISN of Xidian University(991894102)
文摘In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.
