  One key aspect differentiating data-driven single- and multi-channel speech
enhancement and dereverberation methods is that both the problem formulation
and complexity of the solutions are considerably more challenging in the latter
case. Additionally, with limited computational resources, it is cumbersome to
train models that require the management of larger datasets or those with more
complex designs. In this scenario, an unverified hypothesis that single-channel
methods can be adapted to multi-channel scenarios simply by processing each
channel independently holds significant implications, boosting compatibility
between sound scene capture and system input-output formats, while also
allowing modern research to focus on other challenging aspects, such as
full-bandwidth audio enhancement, competitive noise suppression, and
unsupervised learning. This study verifies this hypothesis by comparing the
enhancement promoted by a basic single-channel speech enhancement and
dereverberation model with two other multi-channel models tailored to separate
clean speech from noisy 3D mixes. A direction of arrival estimation model was
used to objectively evaluate its capacity to preserve spatial information by
comparing the output signals with ground-truth coordinate values. Consequently,
a trade-off arises between preserving spatial information with a more
straightforward single-channel solution at the cost of obtaining lower gains in
intelligibility scores.
