Abstract
Two adaptive algorithms are presented for
robust time delay estimation (TDE) in acoustic environments with
a large amount of background noise and reverberation. Recently, an
adaptive eigenvalue decomposition (EVD) algorithm has been
developed for TDE in highly reverberant acoustic environments. In
this paper, we extend the adaptive EVD algorithm to noisy and
reverberant acoustic environments, by deriving an adaptive
stochastic gradient algorithm for the generalized eigenvalue
decomposition (GEVD) or by prewhitening the noisy microphone
signals. We have performed simulations using a localized and a
diffuse noise source for several SNRs, showing that the time
delays can be estimated more accurately using the adaptive GEVD
algorithm than using the adaptive EVD algorithm. In addition, we
have analyzed the sensitivity of the adaptive GEVD algorithm with
respect to the accuracy of the noise correlation matrix estimate,
showing that its performance may be quite sensitive, especially
for low SNR scenarios.