联合神经网络与子带分解结构的非线性预测滤波器

A Nonlinear Prediction Filter Based on Combination of PRNN and Subband Decomposition Structures

基于 PRNN神经网络与 FIR子带滤波器 ,本文提出一种新颖的非线性自适应预测滤波器。子带分解采用便于实时实现的 DCT,文中给出了相应的学习算法。对实际语音信号进行的非线性预测结果表明 ,本文所提出的非线性预测滤波器与原来的相比 ,在降低预测误差动态范围与提高收敛速度等方面都有了十分明显的改善。

A novel nonlinear adaptive prediction filter is presented based on a combination of PRNN and subband FIR filters. The interpolator in the subband is implemented by the use of DCT which is a computationally efficient transform. The corresponding algorithm is presented. Applying the proposed filter to the nonlinear prediction of the actual speech signal shows that the performances are superior over the earlier one in terms of the dynamic range of prediction errors and the convergence.