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基于最小统计与MMSE-LSA的语音增强 被引量:2

Speech Enhancement Based on Minimum Statistics and MMSE-LSA
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摘要 提出了一种基于最小统计及短时对数谱幅度最小均方误差估计单通道语音增强算法,并在此基础上对其进行了修正。噪声功率谱估计不需要VAD进行有无语音的检测,并在每帧数据都进行更新,可跟踪变电平噪声。该算法在MMSE-LSA准则下得到谱增益函数,并考虑到纯净声音信号频谱特性,对增益函数进行了修正。实验结果表明,该算法可有效去除噪声,在消除音乐噪声的同时对语音信号产生很小的失真,并易于实时处理。 A modified single channel speech enhancement algorithm is described based on a spectral gain derived from MMSE-LSA and a minimum noise estimation approach. The proposed method does not need a voice activity detector to estimate the noise and renew the noise estimation in every frame to track varying level noise. Considering the properties of clean speech signal, the spectral gain derived from MMSE-ISA is modified. Extensive testing has shown that this algorithm can be implemented in real time and excellent noise suppression is achieved, while avoiding the musical residual noise phenomena and leading weak speech distortion.
出处 《电声技术》 2009年第8期55-59,共5页 Audio Engineering
关键词 语音增强 最小估计 最小均方误差估计 音乐噪声 speech enhancement minimum statistics MMSE music noise
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参考文献6

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同被引文献18

  • 1国雁萌,付强,颜永红.复杂噪声环境中的语音端点检测[J].声学学报,2006,31(6):549-554. 被引量:16
  • 2张贤达.现代信号处理[M].北京:清华大学出版社,2009:206-209.
  • 3吴三灵,温波,于永强.火炮动力实验[M].北京:国防工业出版社,2004:64-118.
  • 4Martin R.Spectral subtraction based on minimum statistics[C]//Proc.Seventh Eur.Signal Processing Conf.(EUSIPCO'94),1994:1182-1185.
  • 5Martin R.Noise power spectral density estimation based onoptimal smoothing and minimum statistics[J].IEEE Trans.Speech Audio Process,2001 (9):504-512.
  • 6Martin R.Bias compensation methods for minimum statistics noise spectral density estimation[J].Signal Processing,2006,86(6):1215-1219.
  • 7吴三灵;温波;于永强.火炮动力实验[M]北京:国防工业出版社,200464-118.
  • 8张贤达.现代信号处理[M]北京:清华大学出版社,2009.
  • 9Martin R. Spectral subtraction based on minimum statis-tics[A].1994.1182-1185.
  • 10Martin R. Noise power spectral density estimation based on optimal smoothing and minimum statistics[J].IEEE Trans Speech Audio Process,2001.504-512.

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