  With the recent advancements of data driven approaches using deep neural
networks, music source separation has been formulated as an instrument-specific
supervised problem. While existing deep learning models implicitly absorb the
spatial information conveyed by the multi-channel input signals, we argue that
a more explicit and active use of spatial information could not only improve
the separation process but also provide an entry-point for many
user-interaction based tools. To this end, we introduce a control method based
on the stereophonic location of the sources of interest, expressed as the
panning angle. We present various conditioning mechanisms, including the use of
raw angle and its derived feature representations, and show that spatial
information helps. Our proposed approaches improve the separation performance
compared to location agnostic architectures by 1.8 dB SI-SDR in our Slakh-based
simulated experiments. Furthermore, the proposed methods allow for the
disentanglement of same-class instruments, for example, in mixtures containing
two guitar tracks. Finally, we also demonstrate that our approach is robust to
incorrect source panning information, which can be incurred by our proposed
user interaction.
