  Power spectral density (PSD) estimates of various microphone signal
components are essential to many speech enhancement procedures. As speech is
highly non-nonstationary, performance improvements may be gained by maintaining
time-variations in PSD estimates. In this paper, we propose an instantaneous
PSD estimation approach based on generalized principal components. Similarly to
other eigenspace-based PSD estimation approaches, we rely on recursive
averaging in order to obtain a microphone signal correlation matrix estimate to
be decomposed. However, instead of estimating the PSDs directly from the
temporally smooth generalized eigenvalues of this matrix, yielding temporally
smooth PSD estimates, we propose to estimate the PSDs from newly defined
instantaneous generalized eigenvalues, yielding instantaneous PSD estimates.
The instantaneous generalized eigenvalues are defined from the generalized
principal components, i.e. a generalized eigenvector-based transform of the
microphone signals. We further show that the smooth generalized eigenvalues can
be understood as a recursive average of the instantaneous generalized
eigenvalues. Simulation results comparing the multi-channel Wiener filter (MWF)
with smooth and instantaneous PSD estimates indicate better speech enhancement
performance for the latter. A MATLAB implementation is available online.
