  Cinematic audio source separation is a relatively new subtask of audio source
separation, with the aim of extracting the dialogue, music, and effects stems
from their mixture. In this work, we developed a model generalizing the
Bandsplit RNN for any complete or overcomplete partitions of the frequency
axis. Psychoacoustically motivated frequency scales were used to inform the
band definitions which are now defined with redundancy for more reliable
feature extraction. A loss function motivated by the signal-to-noise ratio and
the sparsity-promoting property of the 1-norm was proposed. We additionally
exploit the information-sharing property of a common-encoder setup to reduce
computational complexity during both training and inference, improve separation
performance for hard-to-generalize classes of sounds, and allow flexibility
during inference time with detachable decoders. Our best model sets the state
of the art on the Divide and Remaster dataset with performance above the ideal
ratio mask for the dialogue stem.
