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修正的基于广义Gamma语音模型语音增强算法 被引量:1

Modified speech enhancement algorithm under signal presence probability with generalized Gamma speech model
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摘要 广义Gamma模型是近年来新提出的一种语音分布模型,相对于传统的高斯或超高斯模型具有更好的普适性和灵活性,提出一种基于广义Gamma语音模型和语音存在概率修正的语音增强算法。在假设语音和噪声的幅度谱系数分别服从广义Gamma分布和Gaussian分布的基础上,推导了语音信号对数谱的最小均方误差估计式;在该模型下进一步推导了语音存在概率,对最小均方误差估计进行修正。仿真结果表明,与传统的短时谱估计算法相比,该算法不仅能够进一步提高增强语音的信噪比,而且可以有效减小增强语音的失真度,提高增强语音的主观感知质量。 This paper presents a modified speech enhancement algorithm under signal presence probability. Generalized Gamma distribution priors are assumed for speech short-time spectral amplitudes, which is more flexible in capturing the statistical behavior of speech signals. It derives a Minimum Mean-Square Error(MMSE)estimator of the log-spectra am-plitude for speech signals, under the assumption of a generalized Gamma speech priors and additive Gaussian noise priors. Furthermore, modification under signal presence probability is obtained, which is estimated for each frequency bin and each frame consistent with the new model. The simulation results show that the proposed algorithm achieves better noise suppression and lower speech distortion compared to the conventional short-time spectral amplitude estimators, which are based on Gaussian and super-Gaussian speech model.
出处 《计算机工程与应用》 CSCD 2014年第18期230-235,共6页 Computer Engineering and Applications
关键词 语音增强 语音存在概率 广义Gamma分布 最小均方误差 对数谱 speech enhancement speech presence probability generalized Gamma distribution Minimum Mean-SquareError ( MM SE) log-spectral
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参考文献13

  • 1Ephraim Y,Malah D.Speech enhmcement using a mini- mum mean-square error short-time spectral amplitude esti- mator[J].IEEE Trans on Acoust Speech, Signal Process, 1984,32(6) : 1109-1121.
  • 2Ephraim Y,Malah D.Speech enhancement using a mini- mum mean-square error log-spectral amplitude estimator[J]. IEEE Trans on Acoust Speech,Signal Process,1985,33 (2) :443-445.
  • 3Cohen I.Optimal speech enhancement under signal pres- ence uncertainty using log-spectral amplitude estimator[J]. IEEE Signal Process Lett,2002,9(4).
  • 4Gazor S, Zhang W.Speech probability distribution[J].IEEE Signal Process Lett,2003,10(7).
  • 5Martin R.Speech enhancement based on minimum mean- square error estimation and super gaussian priors[J].IEEE Trans on Speech Audio Process, 2005,13 (5) : 845-856.
  • 6Lotter T,Vary P.Speech enhancement by MAP spectral amplitude estimation using a super-Gaussian speech model[J]. Eurasip J Signal Process,2005(7) : 1110-1126.
  • 7邹霞,陈亮,张雄伟.基于Gamma语音模型的语音增强算法[J].通信学报,2006,27(10):118-123. 被引量:11
  • 8Erkelens J S, Hendriks R C, Heusdens R, et al.Minimum mean-square error estimation of discrete flourier coeffi- cients with generalized Gamma priors[J].IEEE Trans on Audio, Speech, Language Process, 2007,15 (6) : 1741-1752.
  • 9Borgstrom B J,Alwan A.Log-spectral amplitude estima- tion with generalized Gamma distributions for speech enhancement[C]//IEEE Int Conf Acoustic, Speech, Signal Process (ICASSP), Prague, Czech, 2011 : 4756-4759.
  • 10Thomas E, Peter V.Model-based speech enhancement using SNR dependent MMSE estimation[C]//IEEE Int Conf Acoustic, Speech, Signal Process(ICASSP) ,Prague, Czech, 2011:4652-4655.

二级参考文献16

  • 1EPHRAIM Y,MALAH D.Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator[J].IEEE Trans Acoustic,Speech,Signal Processing,1984,32(6):1109-1121.
  • 2EPHRAIM Y,MALAH D.Speech enhancement using a minimum mean-square error log-spectral amplitude estimator[J].IEEE Trans Acoustic,Speech,Signal Processing,1985,33(2):443-445.
  • 3SOON I Y,KOH S N,YEO C K.Noisy speech enhancement using discrete cosine transform[J].Speech Communication,1998,24(3):249-257.
  • 4GAZOR S,ZHANG W.Speech probability distribution[J].IEEE Signal Processing Letters,2003,10(7):204-207.
  • 5MARTIN R.Statistical methods for the enhancement of noisy speech[A].IWAENC'2003[C].2003.1-6.
  • 6MARTIN R.Speech enhancement using MMSE short time spectral estimation with Gamma distributed speech priors[A].ICASSP'2002[C].2002.253-256.
  • 7MARTIN R,BREITHAUPT C.Speech enhancement in the DFT domain using Laplacian speech priors[A].IWAENC'2003[C].2003.87-90.
  • 8BREITHAUPT C,MARTIN R.MMSE estimation of magnitudesquared DFT coefficients with supergaussian priors[A].ICASSP'2003[C].2003.896-899.
  • 9CHEN B,LOIZOU P C.Speech enhancement using a MMSE short time spectral amplitude estimator with Laplacian speech modeling[A].ICASSP'2005[C].2005.1097-1100.
  • 10GAZOR S.Employing Laplacian-Gaussian densities for speech enhancement[A].ICASSP'2004[C].2004.297-300.

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