潘晴,冉福星,李雅昆
Pan Qing,Ran Fuxing,Li Yakun

• 1:广东工业大学信息工程学院

摘要(Abstract)：

针对低信噪比下语音增强困难的问题及经验模态分解(empirical mode decomposition,EMD)在语音增强中的有效应用,提出了一种基于EMD的前后置滤波语音增强算法.将含噪语音信号进行卡尔曼滤波后做EMD分解,采用能量阈值法判断出含有残余噪声的本征模态函数(IMF),对其小波变换后与其余IMF重构为最终的语音增强信号.实验表明,在输入信噪比为-10~5dB的不同噪声环境下,以时域分段信噪比及语音质量感知评测PESQ作为评价指标,效果均优于单独的EMD、小波软阈值法及卡尔曼滤波算法,是一种有效的语音增强算法.
To solve the problem of difficulty in speech enhancement under low SNR conditions,this paper proposes a preand post-filtered speech enhancement algorithm based on empirical mode decomposition.The algorithm first performs Kalman filter on the speech signal containing noise,and then performs empirical mode decomposition.And uses energy threshold algorithm to determine the intrinsic modal function with residual noise.After the wavelet transform,the intrinsic modal function is reconstructed with the rest of the intrinsic modal function to become the final speech enhancement signal.In the experiment,SNR of the noise environment was set within the range of-10 dB to 5 dB,and the time domain segmented signal-to-noise ratio and speech quality perception evaluation were used as evaluation indices.Experimental results show that the effect of speech enhancement is better than that using EMD,wavelet soft thresholding,and Kalman filtering algorithm respectively.

关键词(KeyWords)： 卡尔曼滤波;经验模态分解;能量阈值;小波变换
Kalman filter;empirical mode decomposition;energy threshold;wavelet transform

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(61372173)

作者(Author): 潘晴,冉福星,李雅昆
Pan Qing,Ran Fuxing,Li Yakun

DOI: 10.16366/j.cnki.1000-2367.2018.03.006

参考文献(References)：

• [1]史林军,周佳佳,温荣超,等.基于经验模态分解与小波分析相结合的风电功率平滑控制[J].电力系统保护与控制,2016,44(24):9-16.
• [2]孙曙光,庞毅,刘建强.基于Hilbert频移的EEMD谐波检测方法[J].电力系统保护与控制,2017,45(15):85-91.
• [3]Zhang J C,Zhao L.A speech denoising method based on improved EMD[C]//2011International Conference on Multimedia and Signal Processing.Washington DC:IEEE Computer Society,2011:305-309.
• [4]Z2o L,Coelho R,Flandrin P.Speech enhancement with EMD and Hurst-Based mode selection[J].IEEE/ACM Transactions on Audio,Speech,and Language Processing,2014,22(5):899-911.
• [5]Chatlani N,Soraghan J J.EMD-Based Filtering(EMDF)of low-frequency noise for speech enhancement[J].IEEE Transactions on Audio,Speech,and Language Processing,2012,20(4):1158-1166.
• [6]Khaldi K,Boudraa A O,Turki M.Voiced/unvoiced speech classification-based adaptive filtering of decomposed empirical modes for speech enhancement[J].IET Signal Processing,2016,10(1):69-80.
• [7]Mallat S H,Wang W L.Sigularity detection and processing with wavelet[J].IEEE Transaction on Information Theory,1992,38(2):617-643.
• [8]Gannot S.Speech processing utilizing the Kalman filter[J].IEEE Instrumentation&Measurement Magazine,2012,15(3):10-14.
• [9]Schwartz B,Gannot S,Habets E A P.Online speech dereverberation using kalman filter and EM algorithm[J].IEEE/ACM Transactions on Audio,Speech,and Language Processing,2015,23(2):394-406.
• [10]曹玲芝,李振杰.基于dq变换与小波多分辨率分析的电力系统暂态复合扰动信号检测方法[J].电力系统保护与控制,2016,44(21):138-143.
• [11]Islam M T,Shahnaz C,Zhu W P,et al.Speech enhancement based on student modeling of teager energy operated perceptual wavelet packet coefficients and a custom thresholding function[J].IEEE/ACM Transactions on Audio,Speech,and Language Processing,2015,23(11):1800-1811.
• [12]Martin R.Noise power spectral density estimation based on optimal smoothing and minimum statistics[J].IEEE Transactions on Speech and Audio Processing,2001,9(5):504-512.