神经网络和小波变换融合算法的去噪研究

Devoicing Algorithm Based on Neural Network and Wavelet Transform

传统小波变换阈值选取采用软阈值和硬阈值方法,这两种阈值方法都存在自身局限性,软阈值方法处理后的系数存在偏差,影响信号的稳定性和连续性,硬阈值方法在处理语音信号时易导致pseudo—Gibbs现象,滤波效果粗糙.根据经验公式确定阈值方法存在不确定性,因此本文通过改进的神经网络遗传算法和小波变换算法进行融合,确定最佳阈值,通过去噪实验证明该融合算法的可行性.

Aiming at the traditional wavelet transform threshold, the soft threshold and hard threshold method are adopted. The two threshold methods have their own limitations. There is a deviation in the processing of the soft threshold method, which affects the stability and continuity of the signal. The hard threshold method can easily lead to Gibbs-pseudo in processing speech signals. According to the empirical formula, the threshold method is uncertain, so the fusing algorithm based on neural network genetic algorithm and wavelet transform is put forward. The feasibility of the fusion algorithm is demonstrated by experiments.