改进谱减法结合神经网络的语音增强研究

背景噪声是通信系统噪声干扰的来源之一,语音增强可以降低乃至消除噪声干扰,进而提高语音的可懂度.为了减小复杂噪声环境下谱减法引发的音乐噪声,采取正交的多窗谱估计对语音功率谱平滑处理,有效的减小了信息丢失和估计波动.利用自适应谱减系数调整谱增益和谱下限来控制残留噪声,利用优化的IMCRA算法对噪声及时更新来判决语音段和静音段,同时借助特性良好的BP神经网络方法进行训练,语音和噪声谱通过谱减后,波形重构获取增强的语音信号.仿真结果表明语音降噪效果好、可懂度高.

Research on speech enhancement based on spectral subtraction and neural network

Background noise is one of the noise sources of communication system,speech enhancement can reduce or eliminate noise interference,and improve speech intelligibility.Speech power spectrum is adopted orthogonal multi window spectrum estimation for smoothing processing,to reduce music noise from spectrum subtraction under the complicated noise environment.The loss of information and estimation have effectively reduced.Using adaptive spectral subtraction coefficient to adjust spectrum gain and floor to control the residual noise,using the optimized IMCRA algorithm for noise update to speech and silence,the enhanced speech signal has acquired by spectral subtraction and waveform reconstruction after the deep neural network trained data.The simulation results show that the noise reduction effect of speech is good,speech intelligibility is well.