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计算机工程

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基于听觉掩蔽效应的语音增强算法

蔡军,李飞,张毅   

  1. (重庆国家信息无障碍与服务机器人工程研发中心,重庆 400065)
  • 收稿日期:2016-07-01 出版日期:2017-07-15 发布日期:2017-07-15
  • 作者简介:蔡军(1977—),男,副教授、硕士,主研方向为智能控制器、控制工程、智能机器人;李飞,硕士研究生;张毅,教授、博士后、博士生导师。
  • 基金资助:
    重庆市基础与前沿研究计划重点项目“基于听觉仿生的移动搬运机器人多声源语音识别理论与方法”(cstc2015jcyjBX0066)。

Speech Enhancement Algorithm Based on Auditory Masking Effect

CAI Jun,LI Fei,ZHANG Yi   

  1. (Chongqing National Information Accessibility and Service Robot Engineering R&D Center,Chongqing 400065,China)
  • Received:2016-07-01 Online:2017-07-15 Published:2017-07-15

摘要: 对于低信噪比环境下的语音信号,传统谱减法残留的背景噪声较大。针对该问题,基于听觉掩蔽效应提出一种改进的语音增强算法。将人耳听觉掩蔽特性与功率谱减法相结合,设计一种时域递归平均算法对噪声进行估计,同时对带噪语音信号做频谱相减处理,从听觉的角度出发,利用估计的语音信号功率谱计算掩蔽阈值,并引入谱减功率修正系数和谱减噪声系数,实现带噪语音的信号增强。利用Matlab 2012b进行仿真,实验结果表明,该算法在低信噪比条件下能够较好地抑制背景噪声,改善语音质量,且与改进自适应滤波算法相比,其输出信号的信噪比可提高5%左右。

关键词: 信噪比, 背景噪声, 听觉掩蔽效应, 语音增强, 掩蔽阈值

Abstract: For speech signals in low Signal-to-Noise Ratio(SNR) environment,residual background noise is large when using the traditional spectral subtraction method.Aiming at this problem,this paper puts forward an improved speech enhancement algorithm based on auditory masking effect.By combining the human ear auditory masking properties with power spectrum subtraction method,it puts forward a time domain recursive average algorithm to estimate noise.It makes spectrum subtraction for the speech signal with noise.From the perspective of hearing,it uses the estimated speech signal power spectrum to calculate the masking threshold.Finally,it introduces power correction coefficient spectral spectrum noise reduction coefficient to enhance speech signal with noise.Obtained by Matlab 2012b simulation,the experimental results show that the proposed algorithm can better suppress the background noise and improve the speech quality under low SNR conditions,and the SNR of the output signal is improved by about 5% compared with the improved adaptive filtering algorithm.

Key words: Signal-to-Noise Ratio(SNR), background noise, auditory masking effect, speech enhancement, masking threshold

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