基于改进弦截法的FastICA算法研究

Research on FastICA algorithm based on improved secant method

针对FastICA算法的收敛性易受初始解混矩阵的初值选择影响,引入梯度下降法降低初值选择敏感性,并且提出改进弦截法,加快收敛速度。实验结果显示,基于改进弦截法的FastICA算法与其他FastICA算法相比,不但提高了算法的分离性能,而且减少了迭代次数,增强了收敛稳定性。所以,改进的FastICA算法克服了初值选择敏感的影响,获得更快速、更鲁棒的语音分离性能。

Aiming at the convergence performance of FastICA is easy to affected by initial value selection of the initial demixing matrix,this paper introduced the gradient descent method to reduce initial value sensitivity,and put forward the improved secant method to accelerate the convergence speed. The experimental results show that the improved algorithm compared with other FastICA algorithm,not only improves the separation performance,but also reduces the number of iterations and enhances the convergence stability. Therefore,the improved algorithm overcomes the sensitive influence of initial value selection,and achieves faster and more robust speech separation performance.