The spatio-temporal-prediction (STP) method for multichannel speech enhancement has recently been proposed.\nThis approach makes it theoretically possible to attenuate the residual noise without distorting speech. In addition,\nthe STP method depends only on the second-order statistics and can be implemented using a simple linear filtering\nframework. Unfortunately, some numerical problems can arise when estimating the filter matrix in transients. In such a\ncase, the speech correlation matrix is usually rank deficient, so that no solution exists. In this paper, we propose to\nimplement the spatio-temporal-prediction method using a signal subspace approach. This allows for nullifying the\nnoise subspace and processing only the noisy signal in the signal-plus-noise subspace. As a result, we are able to not\nonly regularize the solution in transients but also to achieve higher attenuation of the residual noise. The experimental\nresults also show that the signal subspace approach distorts speech less than the conventional method.
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