Optimization algorithm of blind source separation of speech signal based on natural gradient
Aiming at the poor stability and the poor separation performance of the natural gradient algorithm in blind source separation of speech signals,the separation degree is used as the weight to improve Pearson correlation coefficient,and the correlation coefficient whose weight has been improved is used as the factor to control the step size,which makes the step-size change more accurate and reduces the influence of stochastic disturbance term on steady state.At the same time,the momentum term's characteristics of"rewarding the consistent gradient and punishing the inconsistent gradient"is fully utilized,and the increase and decrease of the momentum term are determined according to the change direction of the mixed signal separation degree,which further improves the convergence speed of the algorithm.The experimental results show that compared with the existing adaptive step size natural gradient algorithms,the algorithm proposed in this paper has a higher mean value of SNR and a lower variance,which shows that the separation effect of the algorithm is better,the sensitivity to the initial value is reduced,and the robustness is better.Moreover,the crosstalk error curve of the algorithm proposed in this paper needs a less number of iterations to enter the steady state,which indicates that the convergence rate of the algorithm is faster.
speech signal separationnatural gradientadaptive step-sizecorrelation coefficientmomentum term
NETL
NSTL
万方数据
