  Deep learning-based speech enhancement has shown unprecedented performance in
recent years. The most popular mono speech enhancement frameworks are
end-to-end networks mapping the noisy mixture into an estimate of the clean
speech. With growing computational power and availability of multichannel
microphone recordings, prior works have aimed to incorporate spatial statistics
along with spectral information to boost up performance. Despite an improvement
in enhancement performance of mono output, the spatial image preservation and
subjective evaluations have not gained much attention in the literature. This
paper proposes a novel stereo-aware framework for speech enhancement, i.e., a
training loss for deep learning-based speech enhancement to preserve the
spatial image while enhancing the stereo mixture. The proposed framework is
model independent, hence it can be applied to any deep learning based
architecture. We provide an extensive objective and subjective evaluation of
the trained models through a listening test. We show that by regularizing for
an image preservation loss, the overall performance is improved, and the stereo
aspect of the speech is better preserved.
