Diffuse Noise Suppression Using Crystal-Shaped Microphone Arrays
Diffuse Noise Suppression Using Crystal-Shaped Microphone Arrays
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DOI：
10.1109/tasl.2011.2112645
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发表时间：
2011-09
期刊：
影响因子：
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通讯作者：
N. Ito;Hikaru Shimizu;Nobutaka Ono;S. Sagayama
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文献类型：
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作者：
N. Ito;Hikaru Shimizu;Nobutaka Ono;S. Sagayama
本文介绍了一种利用晶体形传声器阵列抑制漫射噪声的新方法。通过最小方差无失真响应波束形成器（MVDR）和随后的维纳后置滤波器对观测信号进行两阶段处理，有效地抑制了漫射噪声，并给出了目标信号的线性最小均方误差（LMMSE）估计。在这个框架中，从噪声观测中准确估计目标信号的短时功率谱和转向矢量是至关重要的。该方法对空间噪声协方差矩阵进行对角化处理，利用去噪后的空间协方差矩阵的非对角项来准确估计目标信号的短时功率谱和转向矢量。我们使用晶体阵列，某些类别的晶体形阵列几何形状，这使得有可能对角化未知的噪声协方差矩阵常数酉矩阵，而不管它的值，只要噪声满足各向同性条件。通过模拟和真实环境噪声的实验表明，所提出的方法在处理真实世界噪声和混响时的性能大大优于以前的方法。
This paper describes novel methods for diffuse noise suppression using crystal-shaped microphone arrays. The two-stage processing of the observed signals by the Minimum Variance Distortionless Response (MVDR) beamformer and the subsequent Wiener post-filter is effective for diffuse noise suppression and gives the linear minimum mean square error (LMMSE) estimator of the target signal. It is essential in this framework to accurately estimate the short-time power spectrum and the steering vectors of the target signal from the noisy observations. Our methods diagonalize the spatial noise covariance matrix and utilizes the denoised off-diagonal entries of the spatial covariance matrix to accurately estimate the short-time power spectrum and the steering vectors of the target signal. We employ crystal arrays, certain classes of crystal-shaped array geometries, which make it possible to diagonalize the unknown noise covariance matrix by a constant unitary matrix regardless of its value as long as noise meets an isotropy condition. It is shown through experiments with simulated and real environmental noise that the proposed methods outperform previous methods substantially for real world noise and in the presence of reverberation.