一种基于改进的量子神经网络的语音降噪方法

A Method to Decrease Noise in Speech Based on Improved Quantum Neural Networks

利用一种改进的量子神经网络(IPSO-QNN)在时域上对语音信号进行降噪处理,重点改进了QNN所涉及到的学习算法.针对粒子群算法本身存在早熟的不足,提出了一种改进的粒子群优化算法(IPSO).通过对早熟粒子的速度和位置叠加随机数据,使其离开局部最优,从而使该算法具有更强的寻优能力.利用IPSO对量子神经网络的参数进行训练和学习,建立了比较高效的基于改进的量子神经网络的语音信号滤波器,并通过Matlab软件建立实验平台,实验结果表明,新算法充分利用了量子神经计算的快速性以及粒子群算法的全局寻优能力,从而使该语音信号滤波器具有良好的降噪性能.

The IPSO-QNN is applied to decreasing noise of speech signal in the time domain,and the emphasis is put on the improvement of the learning algorithm of QNN（quantum neural network）.Aiming at the inherent shortcomings of premature with particle swarm optimization（PSO）,an improved PSO（IPSO） is presented.The new arithmetic has better optimization performance by adding random data to premature particles’ speed and position to make the premature particles leave the local optimum.A more efficient speech signal filter based on IPSO-QNN is established by using IPSO in learning and training of the parameters of QNN,a experimental platform is established using Matlab software,and experimental results show that the new arithmetic make best use of faster quantum neural computation and the global optimization ability of PSO,so that the speech signal filter has good performance in decreasing noise.