中文摘要：

信号盲抽取是盲信号处理领域的热点研究方向，它仅抽取感兴趣的信号，能有效减小运算量，解决盲分离中信号顺序不确定性的难题，因而在生物医学信号分析（如EEG、MEG、fMRI等）、语音和图像识别领域得到广泛应用。针对传统的基于时序结构的盲抽取算法存在较弱的抗噪性和对时延估计误差比较敏感的不足，论文提出了将偏度和时序结构相结合的信号盲抽取算法。该算法首先利用偏度的非对称性来度量分离信号的非高斯性，以减弱噪声，同时减小了传统的利用峭度度量非高斯性方法的运算量；其次利用基音周期作为声音信号的最佳时延估计，以实现对感兴趣信号的盲抽取，将两者结合后使得算法对时延估计误差不敏感，且对噪声更具鲁棒性。仿真实验部分选取了标准TIMIT语料库中一男、两女分别单独朗读同一语句的语音信号，盲抽取的实验结果表明：本文算法与文献3中算法相比具有较好的分离效果且抽取速度快，与文献4中算法相比分离效果相当但大大地提高了抽取速度，从而验证了本文算法的有效性。

英文摘要：

Blind signal extraction is a hot research field of blind signal processing, it can only extract the interesting signals, re- duce the computing consumption effectively, and overcome the difficult problem is that the sequence uncertainty of the separated signals of which the simultaneous separation of blind source separation, it plays an important role in many areas such as biomedical signal anal- ysis and processing （ EEG ： electroencephalogram, MEG ： magnetoencephalography, fMRI ： functional magnetic resonance imaging）, speech and image recognition and enhancement, and so on. The drawback of blind source extraction algorithm based on temporally structure is that too sensitive to the estimation error of the time delay and has weak noise immunity, this paper combining skewness and temporal structure as a new cost function to conquer that defects. First of all, this algorithm uses the characteristic of asymmetry on skewness to evaluate the non-Gaussian to suppress the noise, and when run the algorithm program on computer, compared with the tra- ditional kurtosis measure method this algorithm reduces the computing consumption at the same time, in the next place paper uses the pitch of the expect signal as the best time delay estimation to successful extract the interesting source. When joining the skewness and temporal structure, the algorithm is insensitive to the estimation error and is robust for the noise. After that this paper gives specific op- erational steps of this algorithm. In the blind signal extraction computer simulation and experiments part, three speech signals is select- ed from the standard TIMIT corpus are one man and two women reading the same statement separately, the blind signal extraction exper- imental results indicating that compared with the literature 3, this algorithm has better separation efficiency and have faster computing speed, compared with the literature 4 both the algorithms have better separation efficiency but the algorithm extraction rate of this paper is more quickl