摘要
针对谱减法在低信噪比下音乐噪声较大的缺点,通过分析人耳听觉掩蔽特性,提出一种改进的语音增强算法。在维纳滤波法的基础上结合掩蔽效应调整增益系数,采用非平稳环境下的最小约束递归平均算法进行噪声参数估计,利用最小均方误差准则的最优平滑因子对增强语音进行平滑处理,从而进一步消除音乐噪声。仿真结果表明,与改进谱减法与维纳滤波法相比,该算法在低信噪比情况下能有效抑制背景噪声和残余的音乐噪声,保持较好的语音质量和清晰度。
An improved speech enhancement algorithm is proposed by analysis the human auditory masking properties when a serious problems of residual musical noise brought by the Spectral subtraction in low Signal Noise Ratio（SNR）.The gain parameters are adjusted by combined human auditory masking properties with wiener filter.Noise estimation is used by the Minimum Controlled Recursive Averaging（MCRA） algorithm in non-stationary environment.In order to further eliminate the musical noise,the optimal smoothing factor based on Minimum Mean Square Error（MMSE） is used to smooth the enhanced voice.Simulation results show that compared with the improved spectral subtraction and Wiener filtering method,the algorithm can effectively suppress background noise and residual musical noise as well as maintaining speech quality and intelligibility in low SNR.
出处
《计算机工程》
CAS
CSCD
2013年第8期27-30,37,共5页
Computer Engineering
基金
国家自然科学基金资助重点项目(61134006)
关键词
语音增强
听觉掩蔽效应
最优平滑
维纳滤波
最小约束递归平均法
最小均方误差
speech enhancement
auditory masking effect
optimal smoothing
wiener filtering
Minimum Controlled Recursive Averaging（MCRA） method
Minimum Mean Square Error（MMSE）
