Noise power spectral density estimation based on optimal smoothing and minimum statistics
Noise power spectral density estimation based on optimal smoothing and minimum statistics
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DOI：
10.1109/89.928915
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发表时间：
2001-07-01
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影响因子：
--
通讯作者：
Martin, R
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文献类型：
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作者：
Martin, R
本文描述了一种在给定噪声语音信号时估计非平稳噪声功率谱密度的方法。该方法可以与任何需要估计噪声功率谱密度的语音增强算法相结合。与其他方法相比，我们的方法不使用语音活动检测器，而是跟踪每个频带的频谱最小值，而不区分语音活动和语音停顿。通过最小化每个时间步的条件均方估计误差准则，推导出对带噪声语音信号的功率谱密度进行递推平滑的最优平滑参数，并基于最优平滑的功率谱密度估计和谱最小值统计分析，提出了无偏噪声估计器。估计器非常适合于实时实现。此外，为了提高在非平稳噪声下的性能，我们引入了一种加速谱最小值跟踪的方法。最后，我们在语音增强和各种噪声类型的低比特率语音编码的背景下评估了所提出的方法。
We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector, Instead it tracks spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal, Based on the optimally smoothed power spectral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types.