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Description
The inverse-filtering of acoustic impulse responses (AIRs)
can be achieved with existing methods provided a good estimate
of the channel is available and the observed signals
contain little or no noise. Such assumptions are not generally
valid in practical scenarios, leading to much interest in the
issue of robustness. In particular, channel shortening (CS)
techniques have been shown to be more robust to channel
estimation error than existing approaches. In this paper we
investigate CS using the relaxed multichannel least-squares
(RMCLS) algorithm in the presence of both channel error
and additive noise.
It is shown quantitatively that shortening the acoustic channel
to a few ms duration is more robust than attempting to
equalize the channel fully, giving better resultant sound quality
for dereverberation. A key point of this paper is to provide
an explanation for this added robustness in terms of
the equalization filter gain. We provide simulation results
and results for practical settings using speech recordings and
room impulse response measurements from a real acoustic
environment.