  Speech Enhancement (SE) systems typically operate on monaural input and are
used for applications including voice communications and capture cleanup for
user generated content. Recent advancements and changes in the devices used for
these applications are likely to lead to an increase in the amount of
two-channel content for the same applications. However, SE systems are
typically designed for monaural input; stereo results produced using trivial
methods such as channel independent or mid-side processing may be
unsatisfactory, including substantial speech distortions. To address this, we
propose a system which creates a novel representation of stereo signals called
Custom Mid-Side Signals (CMSS). CMSS allow benefits of mid-side signals for
center-panned speech to be extended to a much larger class of input signals.
This in turn allows any existing monaural SE system to operate as an efficient
stereo system by processing the custom mid signal. We describe how the
parameters needed for CMSS can be efficiently estimated by a component of the
spatio-level filtering source separation system. Subjective listening using
state-of-the-art deep learning-based SE systems on stereo content with various
speech mixing styles shows that CMSS processing leads to improved speech
quality at approximately half the cost of channel-independent processing.
