  Voice activity detection (VAD) remains a challenge in noisy environments.
With access to multiple microphones, prior studies have attempted to improve
the noise robustness of VAD by creating multi-channel VAD (MVAD) methods.
However, MVAD is relatively new compared to single-channel VAD (SVAD), which
has been thoroughly developed in the past. It might therefore be advantageous
to improve SVAD methods with pre-processing to obtain superior VAD, which is
under-explored. This paper improves SVAD through two pre-processing methods, a
beamformer and a spatial target speaker detector. The spatial detector sets
signal frames to zero when no potential speaker is present within a target
direction. The detector may be implemented as a filter, meaning the input
signal for the SVAD is filtered according to the detector's output; or it may
be implemented as a spatial VAD to be combined with the SVAD output. The
evaluation is made on a noisy reverberant speech database, with clean speech
from the Aurora 2 database and with white and babble noise. The results show
that SVAD algorithms are significantly improved by the presented pre-processing
methods, especially the spatial detector, across all signal-to-noise ratios.
The SVAD algorithms with pre-processing significantly outperform a baseline
MVAD in challenging noise conditions.
