A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporat...A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.展开更多
A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency ...A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.展开更多
针对单信道时频重叠信号调制方式的识别分类问题,提出了基于信号相关特性的调制识别方法。在研究所选数字通信信号的相关特性的基础上,提取信号延迟相关和瞬时自相关谱峰的数量、幅度和位置方差作为特征参数,实现了同载频时频重叠双信...针对单信道时频重叠信号调制方式的识别分类问题,提出了基于信号相关特性的调制识别方法。在研究所选数字通信信号的相关特性的基础上,提取信号延迟相关和瞬时自相关谱峰的数量、幅度和位置方差作为特征参数,实现了同载频时频重叠双信号的调制识别。仿真结果表明,在理想高斯白噪声背景下,该方法具有良好的识别性能,当信噪比大于-5 d B时,其正确识别率达到95%以上。展开更多
文摘A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.
基金Supported by the Program for New Century Excellent Talents in University, Ministry of Education, China (Grant No. NCET-05-0803)
文摘A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.
文摘针对单信道时频重叠信号调制方式的识别分类问题,提出了基于信号相关特性的调制识别方法。在研究所选数字通信信号的相关特性的基础上,提取信号延迟相关和瞬时自相关谱峰的数量、幅度和位置方差作为特征参数,实现了同载频时频重叠双信号的调制识别。仿真结果表明,在理想高斯白噪声背景下,该方法具有良好的识别性能,当信噪比大于-5 d B时,其正确识别率达到95%以上。
