Spatial information is important for human perception of speech and sound signals. However, this information is often either distorted or completely neglected in noise reduction because it is challenging, to say the least, to achieve optimal noise reduction and accurate spatial information preservation at the same time. This paper studies the problem of binaural speech enhancement. By jointly diagonalizing the speech and noise correlation matrices, we present a method to construct the noise reduction filter as a linear combination of different eigenvectors, which span a certain subspace of the entire space. A different dimension of the subspace gives a different trade-off between noise reduction and speech/noise spatial information preservation. On the one side, if the dimension is equal to 1, maximum noise reduction is achieved but at the price of significant spatial information distortion. On the other extreme, if the dimension of the subspace is equal to that of the entire space, spatial information is accurately preserved but at the cost of no noise reduction. Therefore, one can achieve different levels of compromises between the amount of noise reduction and the level of speech/noise spatial information preservation by adjusting the dimension of the used subspace.

中文翻译：

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双耳语音增强中降噪与语音/噪声空间信息保留的折衷

空间信息对于人类对语音和声音信号的感知很重要。然而，这些信息在降噪中经常被扭曲或完全被忽略，因为至少可以说，同时实现最佳降噪和准确的空间信息保存具有挑战性。本文研究了双耳语音增强问题。通过联合对角化语音和噪声相关矩阵，我们提出了一种将降噪滤波器构造为不同特征向量的线性组合的方法，这些特征向量跨越整个空间的某个子空间。子空间的不同维度给出了降噪和语音/噪声空间信息保存之间的不同权衡。一方面，如果维度等于 1，实现了最大的降噪，但代价是显着的空间信息失真。在另一个极端，如果子空间的维数等于整个空间的维数，则可以准确保留空间信息，但不会以降噪为代价。因此，可以通过调整所用子空间的维度，在降噪量和语音/噪声空间信息保留水平之间实现不同程度的折衷。