摘要
当利用传统自然梯度算法对所有语音信号都使用同一个激活函数进行分离时,对语音信号的盲源分离效果都不尽理想.针对这一问题,采用基于皮尔逊系统的分段激活函数对传统自然梯度算法进行改进.通过引入皮尔逊系统,将皮尔逊函数与传统激活函数相结合,再利用信号的矩估计方法,分段选择合适的激活函数代入分离矩阵,有效克服了传统语音分离算法的缺点和不足.仿真结果表明,在对实际的语音信号进行分离时,改进算法的性能明显优于传统自然梯度算法,并且在保持了良好收敛速度的同时大大减少了均方误差.
The same activation function is always used to separate all speech signals by means of the conventional natural gradient algorithm. Although blind source separation can be achieved, the separation effect is not ideal. To solve this problem, sub-band activation function was used based on Pearson system to improve natural gradient algorithm. By introducing Pearson system, the Pearson function with conventional activation function was combined. The appropriate activation function was selected according to the method of moments-estimating in each part. Then the selected activation function was brought into the separation matrix. This algorithm effectively overcomes the shortcomings and deficiencies of the conventional separation algorithm for speech signal. The simulation results showed that the performance of the improved algorithm is superior to that of the conventional natural gradient algorithm in the actual speech signal separation. In addition, the mean square error is reduced greatly and good convergence rate is maintained at the same time.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第1期6-9,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(11273001
61273164
61370152)
关键词
盲源分离
自然梯度
语音信号
皮尔逊系统
激活函数
blind source separation
natural gradient
speech signal
Pearson system
activation function
