Narrowband noise estimation in the subspace domain

In speech enhancement algorithms, the noise covariance matrix is normally estimated during silent periods detected using a VAD. In this paper an alternative method is proposed where the subspace complementary to the signal subspace is used to continuously estimate the noise covariance matrix. In addition, for narrowband noise an eigenvector matching algorithm is proposed to exploit interframe information for more reliable estimates.