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首页> 外文期刊>Journal of Computers >Efficient β -order Perceptually Motivated Spectral Amplitude Bayesian Estimator Based On Chidistribution for Speech Enhancement
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Efficient β -order Perceptually Motivated Spectral Amplitude Bayesian Estimator Based On Chidistribution for Speech Enhancement

机译：高效β-基于演讲增强咒语的积极动机谱​​幅度贝叶斯估计

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—The traditional Bayesian estimator of short-time spectral amplitude is based on the minimization of the squared-error cost function under the common Gaussian probability density function (pdf). The Gaussian distribution, however, is not the optimal probability distribution. To overcome this phenomenon, we considered to replace the traditional distribution hypothesis of spectral amplitude of speech in this paper. More precisely, we proposed a β -order perceptive Bayesian spectral amplitude estimator which incorporated the assumption of Super-Gaussian chi-distributed spectral amplitude. The new weighting function incorporated the perceptive property as well as the different importance of the spectral valley and peak. Experiments showed that the proposed estimator can achieve a more significant noise reduction and yield a better spectral estimation over the most of latest enhancement algorithms.
机译：- 短时间频谱幅度的传统贝叶斯估计是基于共同高斯概率密度函数（PDF）下平方误差成本函数的最小化。然而，高斯分布不是最佳概率分布。为了克服这种现象，我们考虑取代本文的传统分布假设的谱幅度的光谱幅度。更确切地说，我们提出了一种β-oder令人感知贝叶斯谱幅度估计器，其结合了超高斯Chi分布式光谱幅度的假设。新加权功能掺入了感知性质以及光谱谷和峰的不同重要性。实验表明，所提出的估计器可以实现更显着的降噪，并在最新的增强算法中产生更好的光谱估计。

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