Resource-constrained stereo singing voice cancellation
Clara Borrelli James Rae Dogac Basaran Matt McVicar Mehrez Souden Matthias Mauch
We study the problem of stereo singing voice cancellation, a subtask of music
source separation, whose goal is to estimate an instrumental background from a
stereo mix. We explore how to achieve performance similar to large
state-of-the-art source separation networks starting from a small, efficient
model for real-time speech separation. Such a model is useful when memory and
compute are limited and singing voice processing has to run with limited
look-ahead. In practice, this is realised by adapting an existing mono model to
handle stereo input. Improvements in quality are obtained by tuning model
parameters and expanding the training set. Moreover, we highlight the benefits
a stereo model brings by introducing a new metric which detects attenuation
inconsistencies between channels. Our approach is evaluated using objective
offline metrics and a large-scale MUSHRA trial, confirming the effectiveness of
our techniques in stringent listening tests.