  This paper considers the problem of audio source separation where the goal is
to isolate a target audio signal (say Alice's speech) from a mixture of
multiple interfering signals (e.g., when many people are talking). This problem
has gained renewed interest mainly due to the significant growth in voice
controlled devices, including robots in homes, offices, and other public
facilities. Although a rich body of work exists on the core topic of source
separation, we find that robotic motion of the microphone -- say the robot's
head -- is a complementary opportunity to past approaches. Briefly, we show
that rotating the microphone array to the correct orientation can produce
desired aliasing between two interferers, causing the two interferers to pose
as one. In other words, a mixture of K signals becomes a mixture of (K-1), a
mathematically concrete gain. We show that the gain translates well to practice
provided two mobility-related challenges can be mitigated. This paper is
focused on mitigating these challenges and demonstrating the end-to-end
performance on a fully functional prototype. We believe that our Rotational
Source Separation module RoSS could be plugged into actual robot heads, or into
other devices (like Amazon Show) that are also capable of rotation.
