In this paper, a new pipelined nonlinear spline adaptive filter (PNSF) is presented for the speech prediction application. The proposed architecture is essentially an improved pipelined cascade model, where each module consists of a FIR filter followed by a spline activation function. Based on minimum mean square error cost and stochastic gradient method, the on-line learning adaptive algorithms for updating the nonlinear and linear weights are derived. We analyze the selection range of the learning rate involved in the learning algorithms to ensure the convergence of the algorithms. Simulations are carried out to evaluate the performance of the PNSF on nonlinear system identification and speech prediction. Experimental results show that the PNSF provides better performance compared to the spline adaptive filter (SAF), joint process filter using pipelined second-order Volterra filter (JPPSOV) and pipelined neural IIR (PNIIR) models.

本文提出了一种新的流水线非线性样条自适应滤波器（PNSF），用于语音预测应用。所提出的体系结构本质上是一种改进的流水线级联模型，其中每个模块都包含一个FIR滤波器和一个样条激活函数。基于最小均方误差代价和随机梯度法，推导了用于更新非线性权重和线性权重的在线学习自适应算法。我们分析了学习算法所涉及的学习率的选择范围，以确保算法的收敛性。进行了仿真，以评估PNSF在非线性系统识别和语音预测方面的性能。实验结果表明，与样条自适应滤波器（SAF）相比，PNSF具有更好的性能，