Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement
Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement
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DOI：
10.1109/tasl.2011.2159711
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发表时间：
2012-01-01
影响因子：
--
通讯作者：
Gerkmann, Timo
中科院分区：
文献类型：
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作者：
Hendriks, Richard C.;Gerkmann, Timo
对于最小方差无失真响应波束形成器（MVDR）或多通道维纳滤波器等多通道降噪算法，需要估计噪声相关矩阵。为了对其进行估计，文献中经常提出使用语音活动检测器（VAD）。然而，使用VAD只能在没有语音的情况下更新估计矩阵。因此，在语音存在时，噪声相关矩阵估计不能以适当的精度随噪声场的变化而变化。这种效果进一步增加，因为在非平稳噪声中，语音活动检测是一项相当困难的任务，并且很可能发生假警报。在本文中，我们提出并分析了一种不使用VAD估计噪声相关矩阵的算法。该算法基于测量噪声输入和噪声参考之间的相关性，可以获得噪声参考，例如，通过将null指向目标源。当与MVDR波束形成器结合使用时，与竞争算法（如广义旁瓣抵消、基于vad的MVDR波束形成器和基于噪声相关矩阵的MVDR）相比，所提出的噪声相关矩阵估计可以产生更精确的波束形成器响应、更大的信噪比改善和更大的仪器预测语音可理解性。
For multi-channel noise reduction algorithms like the minimum variance distortionless response (MVDR) beamformer, or the multi-channel Wiener filter, an estimate of the noise correlation matrix is needed. For its estimation, it is often proposed in the literature to use a voice activity detector (VAD). However, using a VAD the estimated matrix can only be updated in speech absence. As a result, during speech presence the noise correlation matrix estimate does not follow changing noise fields with an appropriate accuracy. This effect is further increased, as in nonstationary noise voice activity detection is a rather difficult task, and false-alarms are likely to occur. In this paper, we present and analyze an algorithm that estimates the noise correlation matrix without using a VAD. This algorithm is based on measuring the correlation of the noisy input and a noise reference which can be obtained, e. g., by steering a null towards the target source. When applied in combination with an MVDR beamformer, it is shown that the proposed noise correlation matrix estimate results in a more accurate beamformer response, a larger signal-to-noise ratio improvement and a larger instrumentally predicted speech intelligibility when compared to competing algorithms such as the generalized sidelobe canceler, a VAD-based MVDR beamformer, and an MVDR based on the noisy correlation matrix.